cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7949859
title: SM2RAIN CHINA: 1 km rainfall estimation for CHINA region derived from SMCI Soil Moisture (2000-2020)

description: <strong>SM2RAIN-SMCI provides rainfall estimates for the whole CHINA at 1 km/daily spatial resolution </strong>by applying SM2RAIN algorithm (<em>Brocca et al., 2014; 2019) </em>to Soil Moisture data from SMCI Soil moisture (<em>https://doi.org/10.5194/essd-14-5267-2022</em>) for the period from January 2000 to December 2020 (21 years). The SM2RAIN-SMCI rainfall dataset (in mm/day) is provided over a regular grid at 0.01-degree sampling. The product represents the accumulated rainfall between the 00:00 and the 23:59 UTC of the indicated day. The uncalibrated SM2RAIN version (https://doi.org/10.5194/hess-26-2481-2022) was adopted to obtain rainfall. Precipitation data from HRLT (https://doi.org/10.5194/essd-14-4793-2022) were adopted to obtain mean climatology. The rainfall dataset is provided in NetCDF format. To simplify data access, the CHINA region is divided in 101 subregion containing 540 (longitude) * 360 (latitude) pixels each.
DOI: 10.5281/zenodo.7949858
title: SM2RAIN CHINA: 1 km rainfall estimation for CHINA region derived from SMCI Soil Moisture (2000-2020)

description: <strong>SM2RAIN-SMCI provides rainfall estimates for the whole CHINA at 1 km/daily spatial resolution </strong>by applying SM2RAIN algorithm (<em>Brocca et al., 2014; 2019) </em>to Soil Moisture data from SMCI Soil moisture (<em>https://doi.org/10.5194/essd-14-5267-2022</em>) for the period from January 2000 to December 2020 (21 years). The SM2RAIN-SMCI rainfall dataset (in mm/day) is provided over a regular grid at 0.01-degree sampling. The product represents the accumulated rainfall between the 00:00 and the 23:59 UTC of the indicated day. The uncalibrated SM2RAIN version (https://doi.org/10.5194/hess-26-2481-2022) was adopted to obtain rainfall. Precipitation data from HRLT (https://doi.org/10.5194/essd-14-4793-2022) were adopted to obtain mean climatology. The rainfall dataset is provided in NetCDF format. To simplify data access, the CHINA region is divided in 101 subregion containing 540 (longitude) * 360 (latitude) pixels each.
DOI: 10.15129/bdce0b29-70f1-46a0-bf27-f3562a4f5f9e
title: Data for: "Expanding the pharmaceutical formulation space in Fused Filament Fabrication 3D printing applications"

description: Dataset to support publication: "Expanding the pharmaceutical formulation space in Fused Filament Fabrication 3D printing applications" Rheology: Temperature sweep and Frequency sweep data - Haake Mars III rotational rheometer; Data is provided in Microsoft excel format. Original data files are available upon request. Hot-melt-extrusion process data - Process 11, Thermo Fisher; Data is provided in excel file format. Uniformity of mass and dimensions, Infill percentage versus tablet core weight data is provided as excel file formats.
DOI: 10.4121/b9c7327e-97b2-4ea2-9b74-18c51f265027
title: Replication data for "Resource-efficient fault-tolerant one-way quantum repeater with code concatenation".

description: Replication data for the paper "Resource-efficient fault-tolerant one-way quantum repeater with code concatenation".<br>This data set contains data that was produced using the following code repository:https://github.com/bernwo/code-concatenated-quantum-repeaterSpecifically, it contains data produced by the script "determine_reencoding_error_probability.py" included there.<br>The data indicates how large the loss and error probabilities are on qubits encoded in a tree code, in case losses and errors are applied to the individual physical qubits that make up the tree.<br>More detailed information about how the data was produced and how it can be processed and interpreted can also be found in the code repository.
DOI: 10.4121/b9c7327e-97b2-4ea2-9b74-18c51f265027.v1
title: Replication data for "Resource-efficient fault-tolerant one-way quantum repeater with code concatenation".

description: Replication data for the paper "Resource-efficient fault-tolerant one-way quantum repeater with code concatenation".<br>This data set contains data that was produced using the following code repository:https://github.com/bernwo/code-concatenated-quantum-repeaterSpecifically, it contains data produced by the script "determine_reencoding_error_probability.py" included there.<br>The data indicates how large the loss and error probabilities are on qubits encoded in a tree code, in case losses and errors are applied to the individual physical qubits that make up the tree.<br>More detailed information about how the data was produced and how it can be processed and interpreted can also be found in the code repository.
DOI: 10.6084/m9.figshare.23301416.v1
title: List of patient/isolates

description: List of the patients and their isolates
DOI: 10.6084/m9.figshare.23301416.v2
title: List of patient/isolates

description: List of the patients and their isolates
DOI: 10.5517/ccdc.csd.cc2ck021
title: CCDC 2191082: Experimental Crystal Structure Determination

description: Related Article: Moegamat Cassiem Joseph, Izak Kotzé, Andrew J. Swarts, Selwyn F Mapolie|2023|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.cc2dccv0
title: CCDC 2215472: Experimental Crystal Structure Determination

description: Related Article: Harindu Rajapaksha, Logan J. Augustine, Sara E. Mason, Tori Z. Forbes|2023|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.cc2dy69w
title: CCDC 2232598: Experimental Crystal Structure Determination

description: Related Article: Harindu Rajapaksha, Logan J. Augustine, Sara E. Mason, Tori Z. Forbes|2023|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.cc2dy6bx
title: CCDC 2232599: Experimental Crystal Structure Determination

description: Related Article: Tori Z. Forbes, Harindu Rajapaksha, Logan J. Augustine|2023|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.cc2f4l39
title: CCDC 2238730: Experimental Crystal Structure Determination

description: Related Article: ganesh sorabad|2023|CSD Communication|||
description: Related Article: Ganesh Shivayogappa Sorabad, Ding-Yah Yang|2023|Org.Lett.|25|2515|doi:10.1021/acs.orglett.3c00683
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.cc2f73c5
title: CCDC 2241156: Experimental Crystal Structure Determination

description: Related Article: YongJiang Yu|2023|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.cc2f732w
title: CCDC 2241147: Experimental Crystal Structure Determination

description: Related Article: Yong Jiang Yu|2023|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.cc2f7b45
title: CCDC 2241366: Experimental Crystal Structure Determination

description: Related Article: Mikko Hänninen, Reijo Sillanpää|2023|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.cc26l241
title: CCDC 2043152: Experimental Crystal Structure Determination

description: Related Article: Valerii Tkachev|2020|CSD Communication|||
description: Related Article: Yulia P. Tupolova, Vladimir E. Lebedev, Denis V. Korchagin, Valery V. Tkachev, Andrey N. Utenyshev, Roman B. Morgunov, Andrei V. Palii, Igor N. Shcherbakov, Sergey M. Aldoshin|2023|New J.Chem.|47|10884|doi:10.1039/D3NJ01481A
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: 10.21954/ou.rd.23177942
title: Analysis of most used cancer mobile applications for supporting self-management

description: Results of an in-depth analysis of functionalities of cancer mobile applications for supporting self-management. <br> The analysis focused on apps elected by users. We selected apps that have more than 5,000 downloads on Google Play Store (note that the Apple App Store does not show the number of downloads). <br> We found 7 applications which fit all the above criteria: Cancer.Net, ChemoWave, CancerAid, Vinehealth, My Cancer Tracker, By Your Side, and ZenOnco. ZenOnco was not considered because it targets the Indian market. <br> The analysis of functionalities focused on: role within the NHS cancer pathway burden of care development of self-management skills <br> Furthermore, the analysis identified common kernels of functionalities: Self-reported or manually-measured PGHD, Automated PGHD collection, Reminders to users, Warnings / Alerts to users, Providing information and educating users, Data and Trend analyses, Connecting users with HCPs or support network <br> The analysis is structured as an Excel spreadsheet (CancerAppsFunctionalities_v2.xlsx)
DOI: 10.21954/ou.rd.23177942.v1
title: Analysis of most used cancer mobile applications for supporting self-management

description: Results of an in-depth analysis of functionalities of cancer mobile applications for supporting self-management. <br> The analysis focused on apps elected by users. We selected apps that have more than 5,000 downloads on Google Play Store (note that the Apple App Store does not show the number of downloads). <br> We found 7 applications which fit all the above criteria: Cancer.Net, ChemoWave, CancerAid, Vinehealth, My Cancer Tracker, By Your Side, and ZenOnco. ZenOnco was not considered because it targets the Indian market. <br> The analysis of functionalities focused on: role within the NHS cancer pathway burden of care development of self-management skills <br> Furthermore, the analysis identified common kernels of functionalities: Self-reported or manually-measured PGHD, Automated PGHD collection, Reminders to users, Warnings / Alerts to users, Providing information and educating users, Data and Trend analyses, Connecting users with HCPs or support network <br> The analysis is structured as an Excel spreadsheet (CancerAppsFunctionalities_v2.xlsx)
DOI: 10.5281/zenodo.8010645
title: Tree-Ring Measurement File: Lodgepole Pine Samples Collected at Steamboat Geyser, Yellowstone National Park

description: Decade format (Tucson format) tree-ring measurement file (0.001 mm) for lodgepole pine core samples collected adjacent to Steamboat Geyser in Yellowstone National Park, Wyoming, USA.
description: Tree-ring data is associated with the manuscript: The Relation Between Decadal Droughts and Eruptions of Steamboat Geyser in Yellowstone National Park, USA (submitted for publication in G-Cubed).
DOI: 10.5281/zenodo.8010565
title: ALICE pinned specimen images and masks

description: Pinned insect images and corresponding label outlines in JSON format. Part of this dataset contains the image set https://doi.org/10.5281/zenodo.3243225. This dataset is used for training a machine learning model for the identification and segmentation of labels from pinned specimens for the ALICE project: https://doi.org/10.31219/osf.io/s2p73.
DOI: 10.5281/zenodo.8010564
title: ALICE pinned specimen images and masks

description: Pinned insect images and corresponding label outlines in JSON format. Part of this dataset contains the image set https://doi.org/10.5281/zenodo.3243225. This dataset is used for training a machine learning model for the identification and segmentation of labels from pinned specimens for the ALICE project: https://doi.org/10.31219/osf.io/s2p73.
DOI: 10.5281/zenodo.8010664
title: Data used by Calenge et al. in "Assessing the dynamics of Mycobacterium bovis infection in three French badger populations"

description: The zip file contains the raw dataset used by Calenge et al. in their model of the <em>Mycobacterium bovis</em> infection in three French badger populations. The README file explains the content of this dataset.
DOI: 10.5281/zenodo.8010663
title: Data used by Calenge et al. in "Assessing the dynamics of Mycobacterium bovis infection in three French badger populations"

description: The zip file contains the raw dataset used by Calenge et al. in their model of the <em>Mycobacterium bovis</em> infection in three French badger populations. The README file explains the content of this dataset.
DOI: 10.17638/datacat.liverpool.ac.uk/2258
title: Single-Molecule Conductance Dataset for a series of T-shaped Spiropyrans and their In-Situ switching to Merocyanines.

description: As in title - raw single-molecule conductance data for a series of compounds.
DOI: 10.5281/zenodo.7625398
title: Data for GECCO2023 Paper "Many-objective (Combinatorial) Optimization is Easy"

description: <strong>Data for Paper "Many-objective (Combinatorial) Optimization is Easy"</strong> <strong>instances.tar.xz</strong> contains 𝜌mnk-landscape instances <strong>metrics.csv</strong> contains the metric-values based on full enumeration <strong>performance.csv</strong> contains the Pareto resolution and the hypervolume of the different algorithms one each instance <strong>performance_neval.csv</strong> contains the number of evaluations performed by PLS <strong>script.R</strong> is the R script for producing the figures <strong>Reference</strong> Arnaud Liefooghe and Manuel López-Ibáñez. 2023. Many-objective (Combinatorial) Optimization is Easy. In Genetic and Evolutionary Computation Conference (GECCO '23), July 15–19, 2023, Lisbon, Portugal. https://doi.org/10.1145/3583131.3590475 <strong>Abstract</strong> It is a common held assumption that problems with many objectives are harder to optimize than problems with two or three objectives. In this paper, we challenge this assumption and provide empirical evidence that increasing the number of objectives tends to reduce the difficulty of the landscape being optimized. Of course, increasing the number of objectives brings about other challenges, such as an increase in the computational effort of many operations, or the memory requirements for storing non-dominated solutions. More precisely, we consider a broad range of multi- and many-objective combinatorial benchmark problems, and we measure how the number of objectives impacts the dominance relation among solutions, the connectedness of the Pareto set, and the landscape multimodality in terms of local optimal solutions and sets. Our analysis shows the limit behavior of various landscape features when adding more objectives to a problem. Our conclusions do not contradict previous observations about the inability of Pareto-optimality to drive search, but we explain these observations from a different perspective. Our findings have important implications for the design and analysis of many-objective optimization algorithms.
description: {"references": ["Arnaud Liefooghe and Manuel L\u00f3pez-Ib\u00e1\u00f1ez. 2023. Many-objective (Combinatorial) Optimization is Easy. In Genetic and Evolutionary Computation Conference (GECCO '23), July 15\u201319, 2023, Lisbon, Portugal. https://doi.org/10.1145/3583131.3590475"]}
DOI: 10.5281/zenodo.7625397
title: Data for GECCO2023 Paper "Many-objective (Combinatorial) Optimization is Easy"

description: <strong>Data for Paper "Many-objective (Combinatorial) Optimization is Easy"</strong> <strong>instances.tar.xz</strong> contains 𝜌mnk-landscape instances <strong>metrics.csv</strong> contains the metric-values based on full enumeration <strong>performance.csv</strong> contains the Pareto resolution and the hypervolume of the different algorithms one each instance <strong>performance_neval.csv</strong> contains the number of evaluations performed by PLS <strong>script.R</strong> is the R script for producing the figures <strong>Reference</strong> Arnaud Liefooghe and Manuel López-Ibáñez. 2023. Many-objective (Combinatorial) Optimization is Easy. In Genetic and Evolutionary Computation Conference (GECCO '23), July 15–19, 2023, Lisbon, Portugal. https://doi.org/10.1145/3583131.3590475 <strong>Abstract</strong> It is a common held assumption that problems with many objectives are harder to optimize than problems with two or three objectives. In this paper, we challenge this assumption and provide empirical evidence that increasing the number of objectives tends to reduce the difficulty of the landscape being optimized. Of course, increasing the number of objectives brings about other challenges, such as an increase in the computational effort of many operations, or the memory requirements for storing non-dominated solutions. More precisely, we consider a broad range of multi- and many-objective combinatorial benchmark problems, and we measure how the number of objectives impacts the dominance relation among solutions, the connectedness of the Pareto set, and the landscape multimodality in terms of local optimal solutions and sets. Our analysis shows the limit behavior of various landscape features when adding more objectives to a problem. Our conclusions do not contradict previous observations about the inability of Pareto-optimality to drive search, but we explain these observations from a different perspective. Our findings have important implications for the design and analysis of many-objective optimization algorithms.
description: {"references": ["Arnaud Liefooghe and Manuel L\u00f3pez-Ib\u00e1\u00f1ez. 2023. Many-objective (Combinatorial) Optimization is Easy. In Genetic and Evolutionary Computation Conference (GECCO '23), July 15\u201319, 2023, Lisbon, Portugal. https://doi.org/10.1145/3583131.3590475"]}
DOI: 10.5281/zenodo.8010644
title: Data for GECCO2023 Paper "Pareto Local Optimal Solutions Networks with Compression, Enhanced Visualization and Expressiveness"

description: <strong>Data for Paper "Pareto Local Optimal Solutions Networks with Compression, Enhanced Visualization and Expressiveness"</strong> <strong>instances.tar.xz</strong> contains 𝜌mnk-landscape instances <strong>metrics.csv</strong> contains the (C)PLOS-net metric-values <strong>performance.csv</strong> contains the performance of the different algorithms on each instance <strong>merged.csv</strong> contains the merged data from the 2 csv files above <strong>Reference</strong> Arnaud Liefooghe, Gabriela Ochoa, Sébastien Verel, and Bilel Derbel. 2023. <strong>Pareto Local Optimal Solutions Networks with Compression, Enhanced Visualization and Expressiveness</strong>. In Genetic and Evolutionary Computation Conference (GECCO ’23), July 15–19, 2023, Lisbon, Portugal. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3583131.3590474 <strong>Abstract</strong> The structure of local optima in multi-objective combinatorial optimization and their impact on algorithm performance are not yet properly understood. In this paper, we are interested in the representation of multi-objective landscapes and their multi-modality. More specifically, we revise and extend the network of Pareto local optimal solutions (PLOS-net), inspired by the well-established local optima network from single-objective optimization. We first define a compressed PLOS-net which allows us to enhance its perception while preserving the important notion of connectedness between local optima. We then study an alternative visualization of the (compressed) PLOS-net that focuses on good-quality solutions, improves the distinction between connected components in the network, and generalizes well to landscapes with more than 2 objectives. We finally define a number of network metrics that characterize the PLOS-net, some of them being strongly correlated with search performance. We visualize and experiment with small-size multi-objective nk-landscapes, and we disclose the effect of PLOS-net metrics against well-established multi-objective local search and evolutionary algorithms.