cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5552684
title: Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality - Collected Data

description: The deposited data files were used to perform the analysis introduced in the paper: Tamas Galli, Francisco Chiclana and Francois Siewe, "Genetic Algorithm Based Fuzzy Inference System for Describing Execution Tracing Quality", Mathematics, MDPI, 2021. The data were collected through an online questionnaire. The questionnaire has been exported in pdf format and uploaded as file: form_data_collection.pdf. The paper above introduces the steps of analysing, processing the data, constructing, pre-validating the model. The final validation was done over the online questionnaire exported and uploaded in pdf format as form_model_validation.pdf. Questionnaire Part 1, data file: all_usecases_wide.csv The CSV file contains the responses for each use case of part 1 of the online questionnaire enclosed. The columns contain the assigned values from the respondents, on a scale [0; 100]. The following variables are linked to each use case: Accuracy, Legibility, DesignAndImplementation, and Security. These form the input variables of execution tracing quality, while the variable Quality designates the quality of execution tracing. Each fifth column is followed by a column UseCase to designate the use case which is described by the previous five columns. The definitions of the variables can be found in the questionnaire. Questionnaire Part 2, data file: all_real_projects_scores.csv The CSV file contains the responses for real projects in part 2 of the online questionnaire enclosed. The columns contain the assigned values from the respondents, on a scale [0; 100]. Six variables are linked to each response: Accuracy, Legibility, DesignAndImplementation, and Security, which form the input variables of execution tracing quality, while the variable Quality designates the quality of execution tracing. In addition, the variable Type indicates the type of the project, such as server application, desktop application, web UI, mobile application, or embedded application. The definitions of the variables can be found in the questionnaire. Questionnaire Part 3, data file: all_extrem_values_wide.csv The CSV file contains the assigned execution tracing quality value to the provided combination of extreme input values in part 3 of the online questionnaire enclosed. The column IDs represent the question IDs in the survey. The definitions of the variables can be found in the questionnaire.<br>
DOI: 10.5281/zenodo.5552683
title: Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality - Collected Data

description: The deposited data files were used to perform the analysis introduced in the paper: Tamas Galli, Francisco Chiclana and Francois Siewe, "Genetic Algorithm Based Fuzzy Inference System for Describing Execution Tracing Quality", Mathematics, MDPI, 2021. The data were collected through an online questionnaire. The questionnaire has been exported in pdf format and uploaded as file: form_data_collection.pdf. The paper above introduces the steps of analysing, processing the data, constructing, pre-validating the model. The final validation was done over the online questionnaire exported and uploaded in pdf format as form_model_validation.pdf. Questionnaire Part 1, data file: all_usecases_wide.csv The CSV file contains the responses for each use case of part 1 of the online questionnaire enclosed. The columns contain the assigned values from the respondents, on a scale [0; 100]. The following variables are linked to each use case: Accuracy, Legibility, DesignAndImplementation, and Security. These form the input variables of execution tracing quality, while the variable Quality designates the quality of execution tracing. Each fifth column is followed by a column UseCase to designate the use case which is described by the previous five columns. The definitions of the variables can be found in the questionnaire. Questionnaire Part 2, data file: all_real_projects_scores.csv The CSV file contains the responses for real projects in part 2 of the online questionnaire enclosed. The columns contain the assigned values from the respondents, on a scale [0; 100]. Six variables are linked to each response: Accuracy, Legibility, DesignAndImplementation, and Security, which form the input variables of execution tracing quality, while the variable Quality designates the quality of execution tracing. In addition, the variable Type indicates the type of the project, such as server application, desktop application, web UI, mobile application, or embedded application. The definitions of the variables can be found in the questionnaire. Questionnaire Part 3, data file: all_extrem_values_wide.csv The CSV file contains the assigned execution tracing quality value to the provided combination of extreme input values in part 3 of the online questionnaire enclosed. The column IDs represent the question IDs in the survey. The definitions of the variables can be found in the questionnaire.<br>
DOI: 10.6073/pasta/932cfbb9451e64a7824e109b59b4cdff
title: Mean daily discharge, stage height and water temperature data for Oksrukuyik Creek from June to September, 1997

description: This dataset is out of date and has been replaced by a multi-year file: Bowden, W. 2020. Stream temperature and discharge measured each summer for Oksrukuyik Creek at Dalton Road crossing, Arctic LTER, Toolik Field Station, Alaska, 1989-2019 Environmental Data Initiative. http://dx.doi.org/10.6073/pasta/93999a64cc4650828f633e2ab5b237fa Oksrukuyik Creek stage height and calculated discharge for the summer of 1997 as well as Oksrukuyik Creek continuous temperature recorded by a Campbell CR10 datalogger. Measurements were taken about 10 meters downstream of the road (2.6k below the original N and P dripper). In Summer 2009, this location moved upstream of the road, do to the construction of a culvert.
DOI: 10.6073/pasta/1016df241fa6f4e14e810f43601c1025
title: Mean daily discharge, stage height and water temperature data for Oksrukuyik Creek from June to September, 1998

description: This dataset is out of date and has been replaced by a multi-year file: Bowden, W. 2020. Stream temperature and discharge measured each summer for Oksrukuyik Creek at Dalton Road crossing, Arctic LTER, Toolik Field Station, Alaska, 1989-2019 Environmental Data Initiative. http://dx.doi.org/10.6073/pasta/93999a64cc4650828f633e2ab5b237fa Oksrukuyik Creek stage height and calculated discharge for the summer of 1998 as well as Oksrukuyik Creek continuous temperature recorded by a Campbell CR10 data logger. Measurements were taken about 10 meters downstream of the road (2.6k below the original N and P dripper). In Summer 2009, this location moved upstream of the road, do to the construction of a culvert.
DOI: 10.6073/pasta/5333e57ed94fa459cc636a601c88d9dd
title: Mean daily discharge, stage height and water temperature data for Oksrukuyik Creek from June to September, 1999

description: This dataset is out of date and has been replaced by a multi-year file: Bowden, W. 2020. Stream temperature and discharge measured each summer for Oksrukuyik Creek at Dalton Road crossing, Arctic LTER, Toolik Field Station, Alaska, 1989-2019 Environmental Data Initiative. http://dx.doi.org/10.6073/pasta/93999a64cc4650828f633e2ab5b237fa Oksrukuyik Creek stage height and calculated discharge for the summer of 1999 as well as Oksrukuyik Creek continuous temperature recorded by a Campbell CR10 data logger. Measurements were taken about 10 meters downstream of the road (2.6k below the original N and P dripper). In Summer 2009, this location moved upstream of the road, do to the construction of a culvert.
DOI: 10.5517/ccdc.csd.cc1zx97f
title: CCDC 1844445: Experimental Crystal Structure Determination

description: Related Article: zhang honggang|2018|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc244llh
title: CCDC 1970626: Experimental Crystal Structure Determination

description: Related Article: Wen-Xiu Lu, Jian Xing, Yijia Sun, Qinge Huang, Zhenwei Deng, Jian-Gang Mao|2021|Org.Biomol.Chem.|19|10210|doi:10.1039/D1OB01996A
DOI: 10.5517/ccdc.csd.cc24ss2t
title: CCDC 1990016: Experimental Crystal Structure Determination

description: Related Article: Dimitrije Mara, Luca Pilia, Maxim Van de Steen, Ivana Miletto, Min Zeng, Kristof Van Hecke, Angela Serpe, Paola Deplano, Rik Van Deun, Flavia Artizzu|2021|J.Mater.Chem.C|9|15641|doi:10.1039/D1TC04191F
DOI: 10.5517/ccdc.csd.cc25k5wt
title: CCDC 2012516: Experimental Crystal Structure Determination

description: Related Article: Wande Zhang, Shah Nawaz, Yue Huang, Wenjing Gong, Xingfu Wei, Jingping Qu, Baomin Wang|2021|Org.Biomol.Chem.|19|10215|doi:10.1039/D1OB01920A
DOI: 10.5517/ccdc.csd.cc272vz5
title: CCDC 2058337: Experimental Crystal Structure Determination

description: Related Article: Jamie A. Cadge, John F. Bower and Christopher A. Russell|2021|Angew.Chem.,Int.Ed.|60|24976|doi:10.1002/anie.202108744
DOI: 10.5517/ccdc.csd.cc27h7ll
title: CCDC 2070229: Experimental Crystal Structure Determination

description: Related Article: Suresh Rajamanickam, Mayank Saraswat, Sugumar Venkataramani, Bhisma K. Patel|2021|Chemical Science|12|15318|doi:10.1039/D1SC04365J
DOI: 10.5517/ccdc.csd.cc27j8rt
title: CCDC 2071226: Experimental Crystal Structure Determination

description: Related Article: Lele Wang, Huabin Han, Lijie Gu, Wenjing Zhang, Junwei Zhao, Qilin Wang|2021|Chemical Science|12|15389|doi:10.1039/D1SC05741C
DOI: 10.5517/ccdc.csd.cc27j8sv
title: CCDC 2071227: Experimental Crystal Structure Determination

description: Related Article: Lele Wang, Huabin Han, Lijie Gu, Wenjing Zhang, Junwei Zhao, Qilin Wang|2021|Chemical Science|12|15389|doi:10.1039/D1SC05741C
DOI: 10.5517/ccdc.csd.cc27j8tw
title: CCDC 2071228: Experimental Crystal Structure Determination

description: Related Article: Lele Wang, Huabin Han, Lijie Gu, Wenjing Zhang, Junwei Zhao, Qilin Wang|2021|Chemical Science|12|15389|doi:10.1039/D1SC05741C
DOI: 10.5517/ccdc.csd.cc27j8wy
title: CCDC 2071230: Experimental Crystal Structure Determination

description: Related Article: Lele Wang, Huabin Han, Lijie Gu, Wenjing Zhang, Junwei Zhao, Qilin Wang|2021|Chemical Science|12|15389|doi:10.1039/D1SC05741C
DOI: 10.5517/ccdc.csd.cc27j8y0
title: CCDC 2071232: Experimental Crystal Structure Determination

description: Related Article: Lele Wang, Huabin Han, Lijie Gu, Wenjing Zhang, Junwei Zhao, Qilin Wang|2021|Chemical Science|12|15389|doi:10.1039/D1SC05741C
DOI: 10.5517/ccdc.csd.cc27j8z1
title: CCDC 2071233: Experimental Crystal Structure Determination

description: Related Article: Lele Wang, Huabin Han, Lijie Gu, Wenjing Zhang, Junwei Zhao, Qilin Wang|2021|Chemical Science|12|15389|doi:10.1039/D1SC05741C
DOI: 10.5517/ccdc.csd.cc27pyd9
title: CCDC 2076671: Experimental Crystal Structure Determination

description: Related Article: Dmitry V. Osipov, Kirill S. Korzhenko, Daria A. Rashchepkina, Alina A. Artemenko, Oleg P. Demidov, Vadim A. Shiryaev, Vitaly A. Osyanin|2021|Org.Biomol.Chem.|19|10156|doi:10.1039/D1OB01377G
DOI: 10.5517/ccdc.csd.cc27q0ff
title: CCDC 2076734: Experimental Crystal Structure Determination

description: Related Article: Dmitry V. Osipov, Kirill S. Korzhenko, Daria A. Rashchepkina, Alina A. Artemenko, Oleg P. Demidov, Vadim A. Shiryaev, Vitaly A. Osyanin|2021|Org.Biomol.Chem.|19|10156|doi:10.1039/D1OB01377G
DOI: 10.5517/ccdc.csd.cc27q0jj
title: CCDC 2076737: Experimental Crystal Structure Determination

description: Related Article: Dmitry V. Osipov, Kirill S. Korzhenko, Daria A. Rashchepkina, Alina A. Artemenko, Oleg P. Demidov, Vadim A. Shiryaev, Vitaly A. Osyanin|2021|Org.Biomol.Chem.|19|10156|doi:10.1039/D1OB01377G
DOI: 10.5517/ccdc.csd.cc27q0xx
title: CCDC 2076749: Experimental Crystal Structure Determination

description: Related Article: Dmitry V. Osipov, Kirill S. Korzhenko, Daria A. Rashchepkina, Alina A. Artemenko, Oleg P. Demidov, Vadim A. Shiryaev, Vitaly A. Osyanin|2021|Org.Biomol.Chem.|19|10156|doi:10.1039/D1OB01377G
DOI: 10.5517/ccdc.csd.cc27qc1d
title: CCDC 2077063: Experimental Crystal Structure Determination

description: Related Article: Dmitry V. Osipov, Kirill S. Korzhenko, Daria A. Rashchepkina, Alina A. Artemenko, Oleg P. Demidov, Vadim A. Shiryaev, Vitaly A. Osyanin|2021|Org.Biomol.Chem.|19|10156|doi:10.1039/D1OB01377G
DOI: 10.5281/zenodo.5137637
title: COSMOS Model Data

description: The archive contains climate model data using the Earth System Model COSMOS in the study of Knorr et al (2021). COSMOS is a fully coupled general circulation model with atmosphere, ocean–sea ice and vegetation components (Roeckner et al. 2003, Marsland et al. 2003, Brovkin et al. 2009). The notation of the individual files in the archive corresponds to the figure numbers in Knorr et al. (2021). The respective file content is described by the corresponding figure caption in the manuscript. File names for the Extended Data Figure data are indicated by the prefix ‘ED’. For the data of Extended Data Figures 5 and 10c please see the main figure data of Fig. 4b-d and Fig. 2b, respectively. For details regarding the model configuration and the experimental design we would like to refer to the Methods in Knorr et al. (2021) and the references therein. References: Brovkin, V., Raddatz, T. Reick, C. H., Claussen, M. & V. Gayler: Global biogeophysical interactions between forest and climate. Geophys. Res. Lett. 36, 1–5 (2009). Knorr, G., Barker, S., Zhang, X., Lohmann, G., Gong, X., P. Gierz, C. Stepanek, L. Stap: A salty deep ocean as a prerequisite for glacial termination, Nature Geoscience (2021). doi: 10.1038/s41561-021-00857-3 Marsland, S. J., Haak, H., Jungclaus, J. H., Latif, M. & F. Röske: The Max-Planck-Institute global ocean/sea ice model with orthogonal curvilinear coordinates. Ocean Model 5, 91–127 (2003). Roeckner, E. et al.: The atmospheric general circulation model ECHAM5 Part 1: Model description. Report, Max-Planck-Institut für Meteorologie 349, 1–127 (2003).
DOI: 10.5281/zenodo.5137636
title: COSMOS Model Data

description: The archive contains climate model data using the Earth System Model COSMOS in the study of Knorr et al (2021). COSMOS is a fully coupled general circulation model with atmosphere, ocean–sea ice and vegetation components (Roeckner et al. 2003, Marsland et al. 2003, Brovkin et al. 2009). The notation of the individual files in the archive corresponds to the figure numbers in Knorr et al. (2021). The respective file content is described by the corresponding figure caption in the manuscript. File names for the Extended Data Figure data are indicated by the prefix ‘ED’. For the data of Extended Data Figures 5 and 10c please see the main figure data of Fig. 4b-d and Fig. 2b, respectively. For details regarding the model configuration and the experimental design we would like to refer to the Methods in Knorr et al. (2021) and the references therein. References: Brovkin, V., Raddatz, T. Reick, C. H., Claussen, M. & V. Gayler: Global biogeophysical interactions between forest and climate. Geophys. Res. Lett. 36, 1–5 (2009). Knorr, G., Barker, S., Zhang, X., Lohmann, G., Gong, X., P. Gierz, C. Stepanek, L. Stap: A salty deep ocean as a prerequisite for glacial termination, Nature Geoscience (2021). doi: 10.1038/s41561-021-00857-3 Marsland, S. J., Haak, H., Jungclaus, J. H., Latif, M. & F. Röske: The Max-Planck-Institute global ocean/sea ice model with orthogonal curvilinear coordinates. Ocean Model 5, 91–127 (2003). Roeckner, E. et al.: The atmospheric general circulation model ECHAM5 Part 1: Model description. Report, Max-Planck-Institut für Meteorologie 349, 1–127 (2003).
DOI: 10.5281/zenodo.5747926
title: PERFORM Dataset; one control subject

description: Functional Magnetic Resonance Imaging (fMRI), electroencephalography (EEG), sleep and nutrition assessments were performed on one male control subject. This registered access dataset is currently accessible on request only.
DOI: 10.34857/0084505
title: UK Crop Microbiome Cryobank \u2013 Community Outreach Presentation

description: A set of slides about the UK Crop Microbiome Cryobank presented at a community outreach event on 15/09/21 at Rothamsted Research, Harpenden, Hertfordshire, UK\r\n\r\nAuthors: Matthew J. Ryan, Timothy H. Mauchline, Nicola Holden, Jacob G. Malone\r\n\r\nThe UK Crop Microbiome Cryobank is a project funded by BBSRC-UKRI and is a collaboration between CABI, Rothamsted Research, Scotland\u2019s Rural College, the John Innes Centre and the James Hutton Institute
DOI: 10.5281/zenodo.5747787
title: Economic potentials of district heating in Austria

description: This dataset includes result data on assessing the economic potential of district heating potentials in Austria. Following elements are included: District_Heating_Grids.csv: Data on district heating grids in Austria by grid Industrial_waste_heat_potentials.csv: Data on industrial waste heat potentials in Austria for two different temperature levels (T1: &gt;= 100°C, T2: &lt; 100°C) by industrial site Power_Plants.csv: Data on thermal power plants and CHP plants in Austria by site Non_economic_dh_areas.csv: Share of district heating areas with higher costs for district heating than for the decentral heat supply mix; FP-financial perspective; EP-economic perspective Economic_DH-Pot.csv: Aggregated economic district heating potential compared to total space heating and hot water demand; FP-financial perspective; EP-economic perspective Heat_supply.csv: Aggregated economic heat supply by energy carrier year; FP-financial perspective; EP-economic perspective DH-Pot-Areas: spatial specification of the concrete district heating areas DH-Pot-AT_Readme.txt: Readme-file including
DOI: 10.6073/pasta/0a599dd64ae648a2de6c3d57069b0b5c
title: Groundwater dependence of riparian woodlands and the disrupting effect of anthropogenically altered streamflow

description: This dataset includes data inputs from public sources, scripts and outputs to evaluate riparian vegetation reliance on groundwater across California from 2015 to 2020. This dataset accompanies the Rohde et al. paper titled, Groundwater dependence of riparian woodlands and the disrupting effect of anthropogenically altered streamflow. The provided scripts process groundwater, vegetation, climate, and streamflow input data from various sources. Further, all output data and statistical analyses are included.
DOI: 10.6073/pasta/c7d2f57249f025298f196d3f4683e373
title: Trait variation, trade-offs, and attributes may contribute to colonization and range expansion of a globally distributed weed

description: Premise of the study Trait variation, trade-offs, and attributes can facilitate colonization and range expansion. We explored how those trait features compare between ancestral and non-native populations of the globally distributed weed Centaurea solstitialis. Methods We measured traits related to survival, size, reproduction, and dispersal in field sampling following major environmental gradients; that of elevation in Anatolia (ancestral range) and that of precipitation in Argentina (non-native range). We also estimated abundance. Key results We found that overall variation in traits in ancestral populations was similar to that in non-native populations. Only one trait, seed mass, displayed greater variation in ancestral than non-native populations; coincidentally, seed mass has been shown to track global range expansion of C. solstitialis. Traits displayed several associations, among which seed mass and number were positively related in both ranges. Many traits varied with elevation in the ancestral range, whereas none varied with precipitation in the non-native one. Interestingly, most traits varying with elevation within the ancestral range also displayed differences in attributes between ancestral and non-native ranges. Unexpectedly, ancestral plants were more fecund than non-native plants, but density was greater in the non-native than ancestral range, indicating that C. solstitialis survives at larger proportions in the non-native than ancestral range. Conclusions Our results suggest that maintaining levels of trait variation in non-native populations comparable to those in ancestral populations, avoiding trait trade-offs, and developing differences in trait attributes between ranges can play a major role in the success of many weeds in novel environments.
DOI: 10.6084/m9.figshare.17105783
title: Hydroperiod and functional dispersion (birds, plants and aquatic macroinvertebrates) in the Prairie Pothole Region of Alberta

description: We provide both data and R Code.<br>The data provide the functional dispersion for birds(y2), plants (y1) and aquatic macroinvertebrates (y2) and pond permanence in wetlands surveyed between 2013-2014 in the Prairie Pothole Region of southern Alberta. To measure the functional dispersion of each taxon, we used Rao’s quadratic entropy, which does use the weighted mean distance of each species to the group centroid, where weights are based on species relative abundances. We used four proxies for wetland pond permanence, and these include approximate number of days a wetland contained water(x1), maximum water depth observed during the survey period(x2), ratio of water amplitude (i.e., maximum – minimum observed water depths) to maximum depth (x3), and an index of evaporative losses to the atmosphere relative to water inputs based on stable isotopes analysis (x4). We relativized both the biological and ponded permanence variables by dividing each cell by the column maximum.<br>The provided code details how we constructed structural equation models using these data.
DOI: 10.6084/m9.figshare.17105768
title: Plant Phytochemistry Influences the Niche Breadth of Oligophagous Parasitoids (Hymenoptera: Braconidae)

description: Alignments, data files, and R scripts. Fasta and nexus files for Bayesian and maximum likelihood phylogenies. Input file for Jane 4 cophylogenetic analysis. R script and input files for PACo cophylogenetic analysis. R script for and input files PCA and Random Forest plant phytochemistry analyses.
DOI: 10.6084/m9.figshare.17105768.v1
title: Plant Phytochemistry Influences the Niche Breadth of Oligophagous Parasitoids (Hymenoptera: Braconidae)

description: Alignments, data files, and R scripts. Fasta and nexus files for Bayesian and maximum likelihood phylogenies. Input file for Jane 4 cophylogenetic analysis. R script and input files for PACo cophylogenetic analysis. R script for and input files PCA and Random Forest plant phytochemistry analyses.
DOI: 10.5281/zenodo.5746218
title: Limitless per Customer: Understanding and Breaking User and Device Fingerprinting in Android

description: Dataset and artifacts for the research work "Limitless per Customer: Understanding and Breaking User & Device Fingerprinting in Android"
DOI: 10.5281/zenodo.5746217
title: Limitless per Customer: Understanding and Breaking User and Device Fingerprinting in Android

description: Dataset and artifacts for the research work "Limitless per Customer: Understanding and Breaking User & Device Fingerprinting in Android"
DOI: 10.6073/pasta/60deae3675ff30109bf7a35cceaf719d
title: Dissolved organic carbon concentrations in White Clay Creek, Pennsylvania, 1977-2017

description: Dissolved organic carbon concentrations were measured over a period of 4 decades in a 3rd-order reach of White Clay Creek, a stream with a forested riparian zone in the Southeastern Pennsylvania Piedmont. Stream water was filtered through pre-combusted glass fiber filters and analyzed in a variety of dissolved organic carbon analyzers. The data have been used to investigate organic matter biogeochemistry and the role of dissolved organic matter in supporting heterotrophic metabolism in the stream.