cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6300863
title: Identification of Performance Changes at Code Level (Measurement Configuration Dataset)

description: <strong>Measurement Configuration Dataset </strong> <em>This is the anonymous reviewing version; the source code repository will be added after the review.</em> This dataset provides reproduction data for performance measurement configuration at source code level in Java. The measurement data can be obtained using the precision-experiments repository https://anonymous.4open.science/r/precision-experiments-C613/ (Examining Different Repetition Counts) yourself. These data conatained here are the data we obtained from execution on i7-4770 CPU @ 3.40GHz. The analysis was tested on Ubuntu 20.04 and gnuplot 5.2.8. It will not work with older gnuplot versions. To execute the analysis, extract the data by <pre><code class="language-bash">tar -xvf basic-parameter-comparison.tar tar -xvf parallel-sequential-comparison.tar</code></pre> and afterwards build the precision-experiments repo and execute the analysis by <pre><code class="language-bash">cd precision-experiments/precision-analysis/ ../gradlew fatJar cd scripts/configuration-analysis/ ./executeCompleteAnalysis.sh ../../../../basic-parameter-comparison ../../../../parallel-sequential-comparison</code></pre> Afterwards, the following files will be present: <strong>precision-experiments/precision-analysis/scripts/configuration-analysis/repetitionHeatmaps/heatmap_all_en.pdf</strong> (Heatmaps for different repetition counts) <strong>precision-experiments/precision-analysis/scripts/configuration-analysis/repetitionHeatmaps/heatmap_outlierRemoval_en.pdf </strong>(Heatmap with and without outlier removal for 1000 repetitions) <strong>precision-experiments/precision-analysis/scripts/configuration-analysis/histogram_outliers_en.pdf</strong> (Histogram of the outliers) <strong>precision-experiments/precision-analysis/scripts/configuration-analysis/heatmap_parallel_en.pdf </strong>(Heatmap with sequential and parallel execution)
description: Data will be de-anonymized after reviews
DOI: 10.5281/zenodo.6973716
title: Latvia: Behaviours and attitudes in response to the Covid-19 pandemics

description: Web-based survey which was conducted in Latvia in 2020. The survey assesses the sociodemographic effects of the COVID-19 outbreak in Latvia. This survey is titled “Behaviours and attitudes in response to the Covid-19 pandemics”.
DOI: 10.5281/zenodo.6973715
title: Latvia: Behaviours and attitudes in response to the Covid-19 pandemics

description: Web-based survey which was conducted in Latvia in 2020. The survey assesses the sociodemographic effects of the COVID-19 outbreak in Latvia. This survey is titled “Behaviours and attitudes in response to the Covid-19 pandemics”.
DOI: 10.6084/m9.figshare.19867795
title: VCF_file_experimentally_evolved_Shoepeg_pop_2020_gen3

description: This is the VCF file with SNP markers produced from GBS sequence data from an experimental evolution study with divergent replicated selection. In total we had four subpopulations, two subpopulations were selected for tall plant height, and two subpopulations were selected for short plant height. Each subpopulation has a population size of 5000 plants. After 3 generations of selection, we randomly chooses 96 plants in each subpopulation and genotyped and phenotyped them. This VCF file contains the SNP markers, which were called in a SNP calling pipeline after GBS from those 384 single plants.
DOI: 10.5281/zenodo.6549361
title: Data repository - Land use change and carbon emissions of a transformation to timber cities

description: Data and model source code for the publication: Land use change and carbon emissions of a transformation to timber cities<br> (Nature Communications, 2022)<br> DOI: 10.1038/s41467-022-32244-w Abhijeet Mishra1,2,*, Florian Humpenöder1, Galina Churkina1, Christopher P.O. Reyer1, Felicitas Beier1,2, Benjamin Leon Bodirsky1, Hans Joachim Schellnhuber1, Hermann Lotze-Campen1,2, and Alexander Popp1 1 Potsdam Institute for Climate Impact Research (PIK), Member of Leibniz Association, P.O.Box 60 12 03, 14412,6<br> Potsdam, Germany<br> 2 Humboldt University of Berlin, Department of Agricultural Economics, Unter den Linden 6, 10099 Berlin,8<br> Germany Abhijeet Mishra<br> *mishra@pik-potsdam.de<br> May 2022 See README.txt for further details.
DOI: 10.6084/m9.figshare.20448681
title: Plant_height_measurements_across_all_years

description: In total, 384 (96 x 4) individual plants out of approximately 20,000 (5,000 x 4) plants were measured in all four subpopulations. Based on these single plant height measurements, a truncation threshold corresponding to approximately the 5% shortest or 5% tallest plants were calculated for each subpopulation. Two subpopulations were selected for short plants, and two were selected for tall plants. Only the ~ 5% shortest/tallest plants from each subpopulation were harvested as females to generate the next generation. This procedure was repeated in 2017, 2018 and 2020. Ultimately, we generated four experimentally evolved subpopulations, two comprised of short-selected plants and two comprised of tall-selected plants. The plant height measurements from all those 384 single plants in 2016, 2017, 2018 and 2020 are contained in this data set.
DOI: 10.6084/m9.figshare.20448681.v1
title: Plant_height_measurements_across_all_years

description: In total, 384 (96 x 4) individual plants out of approximately 20,000 (5,000 x 4) plants were measured in all four subpopulations. Based on these single plant height measurements, a truncation threshold corresponding to approximately the 5% shortest or 5% tallest plants were calculated for each subpopulation. Two subpopulations were selected for short plants, and two were selected for tall plants. Only the ~ 5% shortest/tallest plants from each subpopulation were harvested as females to generate the next generation. This procedure was repeated in 2017, 2018 and 2020. Ultimately, we generated four experimentally evolved subpopulations, two comprised of short-selected plants and two comprised of tall-selected plants. The plant height measurements from all those 384 single plants in 2016, 2017, 2018 and 2020 are contained in this data set.
DOI: 10.5281/zenodo.6551236
title: MAgPIE model - Land use change and carbon emissions of a transformation to timber cities

description: MAgPIE source code for reproducibility Land use change and carbon emissions of a transformation to timber cities<br> (Nature Communications, 2022) DOI: 10.1038/s41467-022-32244-w Abhijeet Mishra1,2,*, Florian Humpenöder1, Galina Churkina1, Christopher P.O. Reyer1, Felicitas Beier1,2, Benjamin Leon Bodirsky1, Hans Joachim Schellnhuber1, Hermann Lotze-Campen1,2, and Alexander Popp1 1 Potsdam Institute for Climate Impact Research (PIK), Member of Leibniz Association, P.O.Box 60 12 03, 14412,6<br> Potsdam, Germany<br> 2 Humboldt University of Berlin, Department of Agricultural Economics, Unter den Linden 6, 10099 Berlin,8<br> Germany Abhijeet Mishra<br> *mishra@pik-potsdam.de<br> May 2022 See README.md for further details.
DOI: 10.5281/zenodo.6973846
title: Assessment of Ecological Water Scarcity in China(2016-2019)

description: BWR and water consumption data(BWF) from 2016 to 2019 were collected from Provincial water resource bulletin (Ministry of Water Resources of China 2017, 2018, 2019, 2020). GWF was calculated by pollutant loads of each province from China’s Environmental Statistical Yearbooks (National Bureau of Statistics of China 2017, 2018, 2019, 2020), and pollutant included total nitrogen (TN), total phosphorus (TP), ammonia-nitrogen (NH<sub>3</sub>-N), and chemical oxygen demand (COD).
DOI: 10.5281/zenodo.6796474
title: Assessment of Ecological Water Scarcity in China(2016-2019)

description: BWR and water consumption data(BWF) from 2016 to 2019 were collected from Provincial water resource bulletin (Ministry of Water Resources of China 2017, 2018, 2019, 2020). GWF was calculated by pollutant loads of each province from China’s Environmental Statistical Yearbooks (National Bureau of Statistics of China 2017, 2018, 2019, 2020), and pollutant included total nitrogen (TN), total phosphorus (TP), ammonia-nitrogen (NH<sub>3</sub>-N), and chemical oxygen demand (COD).
DOI: 10.5281/zenodo.6973636
title: Construction of a necroptosis-related lncRNA signature to predict the prognosis and immune microenvironment of IDH1 wild-type GBM

description: Original data:Construction of a necroptosis-related lncRNA signature to predict the prognosis and immune microenvironment of IDH1 wild-type GBM.
DOI: 10.5281/zenodo.6973635
title: Construction of a necroptosis-related lncRNA signature to predict the prognosis and immune microenvironment of IDH1 wild-type GBM

description: Original data:Construction of a necroptosis-related lncRNA signature to predict the prognosis and immune microenvironment of IDH1 wild-type GBM.
DOI: 10.5517/ccdc.csd.cc2bclkw
title: CCDC 2156098: Experimental Crystal Structure Determination

description: Related Article: David L. Cousins, Yee Hwee Lim, Joseph P. A. Harrity|2022|J.Org.Chem.|87|9764|doi:10.1021/acs.joc.2c00800
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.cc2bcllx
title: CCDC 2156099: Experimental Crystal Structure Determination

description: Related Article: David L. Cousins, Yee Hwee Lim, Joseph P. A. Harrity|2022|J.Org.Chem.|87|9764|doi:10.1021/acs.joc.2c00800
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.cc2bclmy
title: CCDC 2156100: Experimental Crystal Structure Determination

description: Related Article: David L. Cousins, Yee Hwee Lim, Joseph P. A. Harrity|2022|J.Org.Chem.|87|9764|doi:10.1021/acs.joc.2c00800
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.cc2bf1c6
title: CCDC 2157487: Experimental Crystal Structure Determination

description: Related Article: David L. Cousins, Yee Hwee Lim, Joseph P. A. Harrity|2022|J.Org.Chem.|87|9764|doi:10.1021/acs.joc.2c00800
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: 10.5281/zenodo.6973930
title: Host developmental stages shape the evolution of a plant RNA virus

description: Datasets used in the generation of figures 1 and 2 of: Melero, I., González, R., Elena, S.F. 2022. Host developmental stages shape the evolution of a plant RNA Virus. Philos. Trans. R. Soc. B doi: 10.1098/rtsb.2022.0005
DOI: 10.5281/zenodo.6973929
title: Host developmental stages shape the evolution of a plant RNA virus

description: Datasets used in the generation of figures 1 and 2 of: Melero, I., González, R., Elena, S.F. 2022. Host developmental stages shape the evolution of a plant RNA Virus. Philos. Trans. R. Soc. B doi: 10.1098/rtsb.2022.0005
DOI: 10.5281/zenodo.6973994
title: Training Data for "DeepCLEM: automated registration for correlative light and electron microscopy using deep learning"

description: <strong>This folder contains the training dataset used for the paper</strong> "DeepCLEM: automated registration for correlative light and electron microscopy using deep learning" <em>Rick Seifert, Sebastian M. Markert, Sebastian Britz, Veronika Perschin, Christoph Erbacher, Christian Stigloher and Philip Kollmannsberger</em> F1000Research 9:1275 (2020), https://f1000research.com/articles/9-1275 ------------------------------------------------------------ These are 117+4 manually aligned CLEM images of C.elegans acquired by Sebastian M. Markert, Sebastian Britz and Rick Seifert in the Electron Microscopy Facility of the Biocenter of University of Wuerzburg, Germany. For details and experimental protocols, please see the paper linked above. Contents: "fluo_training": Fluorescence microscopic channel of the 117 training images "sem_training": Scanning electron microscopic channel of the 117 training images "fluo_validation": Fluorescence microscopic channel of the 4 validation images "sem_validation": Scanning electron microscopic channel of the 4 validation images License: CC-BY 4.0
DOI: 10.5281/zenodo.6973993
title: Training Data for "DeepCLEM: automated registration for correlative light and electron microscopy using deep learning"

description: <strong>This folder contains the training dataset used for the paper</strong> "DeepCLEM: automated registration for correlative light and electron microscopy using deep learning" <em>Rick Seifert, Sebastian M. Markert, Sebastian Britz, Veronika Perschin, Christoph Erbacher, Christian Stigloher and Philip Kollmannsberger</em> F1000Research 9:1275 (2020), https://f1000research.com/articles/9-1275 ------------------------------------------------------------ These are 117+4 manually aligned CLEM images of C.elegans acquired by Sebastian M. Markert, Sebastian Britz and Rick Seifert in the Electron Microscopy Facility of the Biocenter of University of Wuerzburg, Germany. For details and experimental protocols, please see the paper linked above. Contents: "fluo_training": Fluorescence microscopic channel of the 117 training images "sem_training": Scanning electron microscopic channel of the 117 training images "fluo_validation": Fluorescence microscopic channel of the 4 validation images "sem_validation": Scanning electron microscopic channel of the 4 validation images License: CC-BY 4.0
DOI: 10.6084/m9.figshare.20448849
title: data.xlsx

description: liver <strong>metastases</strong>
DOI: 10.6084/m9.figshare.20448849.v1
title: data.xlsx

description: liver <strong>metastases</strong>
DOI: 10.5281/zenodo.6974039
title: Datasets for Background and Shading Correction of Optical Microscopy Images by BaSiC -- Downsampled Version

description: This repository holds downsampled example data for publication: "<strong>A BaSiC tool for background and shading correction of optical microscopy images, Nature Communications (2017)</strong>" DOI: https://doi.org/10.1038/ncomms14836. For full-resolution testing data, please refer to Zenodo repository at DOI: 10.5281/zenodo.6334810.
DOI: 10.5281/zenodo.6974038
title: Datasets for Background and Shading Correction of Optical Microscopy Images by BaSiC -- Downsampled Version

description: This repository holds downsampled example data for publication: "<strong>A BaSiC tool for background and shading correction of optical microscopy images, Nature Communications (2017)</strong>" DOI: https://doi.org/10.1038/ncomms14836. For full-resolution testing data, please refer to Zenodo repository at DOI: 10.5281/zenodo.6334810.
DOI: 10.5281/zenodo.6796849
title: Technical Leverage Dataset for Java Dependencies in Maven

description: In finance, leverage is the ratio between assets borrowed from others and one's own assets. A matching situation is present in software: by using free open-source software (FOSS) libraries a developer leverages on other people's code to multiply the offered functionalities with a much smaller own codebase. In finance as in software, leverage magnifies profits when returns from borrowing exceed costs of integration, but it may also magnify losses, in particular in the presence of security vulnerabilities. We aim to understand the level of technical leverage in the FOSS ecosystem and whether it can be a potential source of security vulnerabilities. Also, we introduce two metrics change distance and change direction to capture the amount and the evolution of the dependency on third-party libraries. Our analysis published in [1] shows that small and medium libraries (less than 100KLoC) have disproportionately more leverage on FOSS dependencies in comparison to large libraries. We show that leverage pays off as leveraged libraries only add a 4% delay in the time interval between library releases while providing four times more code than their own. However, libraries with such leverage (i.e., 75% of libraries in our sample) also have 1.6 higher odds of being vulnerable in comparison to the libraries with lower leverage. This dataset is the original dataset used in the publication [1]. It includes 8494 distinct library versions from the FOSS Maven-based Java libraries An online demo for computing the proposed metrics for real-world software libraries is also available under the following URL: https://techleverage.eu/. The original publication is [1]. An executive summary of the results is avialble as the publication [2]. This work has been funded by the European Union with the project AssureMOSS (https://www.assuremoss.eu). [1] Massacci, F., & Pashchenko, I. (2021, May). Technical leverage in a software ecosystem: Development opportunities and security risks. In <em>2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)</em> (pp. 1386-1397). IEEE. [2] Massacci, F., & Pashchenko, I. (2021). Technical Leverage: Dependencies Are a Mixed Blessing. <em>IEEE Secur. Priv.</em>, <em>19</em>(3), 58-62.
description: {"references": ["Massacci, F., & Pashchenko, I. (2021, May). Technical leverage in a software ecosystem: Development opportunities and security risks. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) (pp. 1386-1397). IEEE.", "Massacci, F., & Pashchenko, I. (2021). Technical Leverage: Dependencies Are a Mixed Blessing. IEEE Secur. Priv., 19(3), 58-62."]}
DOI: 10.5281/zenodo.6645685
title: Replication Package for "Evaluating the layout quality of UML class diagrams using machine learning"

description: Open Science material including dataset and replication instructions accompanying the article "Evaluating the layout quality of UML class diagrams using machine learning."
description: The research of Fadhl Hujainah and Michel Chaudron is supported by VINNOVA Grant 2018-05010 (TrafCloud).
DOI: 10.5281/zenodo.6796848
title: Technical Leverage Dataset for Java Dependencies in Maven

description: In finance, leverage is the ratio between assets borrowed from others and one's own assets. A matching situation is present in software: by using free open-source software (FOSS) libraries a developer leverages on other people's code to multiply the offered functionalities with a much smaller own codebase. In finance as in software, leverage magnifies profits when returns from borrowing exceed costs of integration, but it may also magnify losses, in particular in the presence of security vulnerabilities. We aim to understand the level of technical leverage in the FOSS ecosystem and whether it can be a potential source of security vulnerabilities. Also, we introduce two metrics change distance and change direction to capture the amount and the evolution of the dependency on third-party libraries. Our analysis published in [1] shows that small and medium libraries (less than 100KLoC) have disproportionately more leverage on FOSS dependencies in comparison to large libraries. We show that leverage pays off as leveraged libraries only add a 4% delay in the time interval between library releases while providing four times more code than their own. However, libraries with such leverage (i.e., 75% of libraries in our sample) also have 1.6 higher odds of being vulnerable in comparison to the libraries with lower leverage. This dataset is the original dataset used in the publication [1]. It includes 8494 distinct library versions from the FOSS Maven-based Java libraries An online demo for computing the proposed metrics for real-world software libraries is also available under the following URL: https://techleverage.eu/. The original publication is [1]. An executive summary of the results is avialble as the publication [2]. This work has been funded by the European Union with the project AssureMOSS (https://www.assuremoss.eu). [1] Massacci, F., & Pashchenko, I. (2021, May). Technical leverage in a software ecosystem: Development opportunities and security risks. In <em>2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)</em> (pp. 1386-1397). IEEE. [2] Massacci, F., & Pashchenko, I. (2021). Technical Leverage: Dependencies Are a Mixed Blessing. <em>IEEE Secur. Priv.</em>, <em>19</em>(3), 58-62.
description: {"references": ["Massacci, F., & Pashchenko, I. (2021, May). Technical leverage in a software ecosystem: Development opportunities and security risks. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) (pp. 1386-1397). IEEE.", "Massacci, F., & Pashchenko, I. (2021). Technical Leverage: Dependencies Are a Mixed Blessing. IEEE Secur. Priv., 19(3), 58-62."]}
DOI: 10.5281/zenodo.6645684
title: Replication Package for "Evaluating the layout quality of UML class diagrams using machine learning"

description: Open Science material including dataset and replication instructions accompanying the article "Evaluating the layout quality of UML class diagrams using machine learning."
description: The research of Fadhl Hujainah and Michel Chaudron is supported by VINNOVA Grant 2018-05010 (TrafCloud).
DOI: 10.34740/kaggle/dsv/4044089
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.6084/m9.figshare.19709068
title: Neuroevolution-Based Generation of Tests and Oracles for Games

description: Game-like programs have become increasingly popular in many software engineering domains such as mobile apps, web applications, or programming education. However, creating tests for programs that have the purpose of challenging human players is a daunting task for automatic test generators. Even if test generation succeeds in finding a relevant sequence of events to exercise a program, the randomized nature of games means that it may neither be possible to reproduce the exact program behavior underlying this sequence, nor to create test assertions checking if observed randomized game behavior is correct. To overcome these problems, we propose Neatest, a novel test generator based on the NeuroEvolution of Augmenting Topologies (NEAT) algorithm. Neatest systematically explores a program’s statements, and creates neural networks that operate the program in order to reliably reach each statement—that is, Neatest learns to play the game in a way to reliably reach different parts of the code. As the networks learn the actual game behavior, they can also serve as test oracles by evaluating how surprising the observed inputs of a program under test are compared to the inputs obtained on a supposedly correct version of the program. We evaluate this approach in the context of Scratch, an educational programming environment. Our empirical study on 25 non-trivial Scratch games demonstrates that our approach can successfully train neural networks that are not only far more resilient to random influences than traditional test suites consisting of static input sequences, but are also highly effective with an average mutation score of more than 70%.