cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21897012.v2
title: Archaeological Soil Micromorphology dataset from excavations of the Campus of Cartuja, Granada, Spain, in 2013 and 2014

description: Soil micromorphology data from the analysis of samples collected during excavations by the University of Granada on the campus of Cartuja during 2013 and 2014. Analysis of the samples was supported by AHRC ‘Landscapes of (Re)Conquest’ project (Grant no. AH/R013861/1, PI Pluskowski, Co-PIs García-Contreras Ruiz and (Michelle) Alexander). <br> Micromorphological investigation was carried out using a Leica DMLP polarising microscope at magnifications of x40 - x400 under Plane Polarised Light (PPL), Crossed Polarised Light (XPL), and where appropriate Oblique Incident Light (OIL). Thin-section description is conducted using the identification and quantification criteria set out by Bullock <em>et al</em>. (1985) and Stoops (2003), with reference to Courty <em>et al</em>. (1989), Mackenzie and Adams (1994) and Mackenzie and Guilford (1980) for rock and mineral identification, Nicosia and Stoops (2017) for anthropogenic inclusions, and Stoops <em>et al</em>. (2018) for further identification of post-depositional alterations. The tables of results use the descriptions, inclusions and interpretations format following Bullock <em>et al.</em> (1985). <br> <strong>List of tables:</strong> Summary Table Cartuja, Granada, Spain (LoR Project) Micromorphology Summary Table 1 Cartuja, Granada Spain (LoR Project) Micromorphology: Deposit type descriptions Table 2 Cartuja, Granada (LoR Project) Micromorphology: Abundance of inclusions (% of area of thin section). Table 3 Cartuja, Granada (LoR Project) Micromorphology: abundance of post-depositional alterations and pedofeatures <br> <strong>References</strong> <strong>Bullock, P., Fedoroff, N. Jongerius, A. Stoops, G. Tursina, T. 1985–</strong> Handbook for Thin Section Description. Wolverhampton: Waine Research. <strong>Courty, M. A., Goldberg, P., Macphail, R. 1989</strong> <strong>–</strong> Soils and Micromorphology in Archaeology. Cambridge: Cambridge University Press. <strong>Mackenzie, W. S., Adams, A. E. 1994</strong> <em><strong>–</strong></em> <em>A Colour Atlas of Rocks and Minerals in Thin Section</em>. New York: John Wiley and Sons. <strong>Mackenzie, W. S., Guilford, C. 1980</strong> <em><strong>–</strong></em> <em>Atlas of Rock Forming Minerals.</em> Harlow: Longman Group Ltd. <strong>Nicosia, C., Stoops, G. 2017</strong> <em><strong>–</strong></em> <em>Archaeological Soil and Sediment Micromorphology.</em> Chichester: John Wiley & Sons. <strong>Stoops, G. 2003 </strong><em><strong>–</strong></em> Guidelines for Analysis and Description of Soil Thin Sections. Madison: Soil Science Society of America. <strong>Stoops, G., Marcelino, V., Mees, F. 2018</strong> <em><strong>–</strong></em> <em>Interpretation of Micromorphological Features of Soils and Regoliths</em>. Amsterdam: Elsevier <br>
DOI: 10.34740/kaggle/dsv/4977450
title: NSE-Scripts-Data
DOI: 10.34740/kaggle/dsv/4977466
title: Nifty-50 & Nifty Future 50 Dataset
DOI: 10.5281/zenodo.7629970
title: When to be Discrete: Analyzing Algorithm Performance on Discretized Continuous Problems - Data and Code

description: This repository contains the code and data for reproducibility of the paper 'Modular Differential Evolution'. The following files are included: - experiments: Python files which were used to create the discretized functions and collect the data (the experiment_runner files) and the code for each of the tested algorithms (two subfolders) - Process_discr: code which takes the raw IOH data and turns it into the relevant csv-files which are used to create the figures. CSV files are included 'csv_fb_*.zip'. ecdf_creation_script is used to extract ecdf and ert data for the ECDF_plot notebook. This data is added as 'other_csvs.zip' - Plot*: notebooks which are used to generate the figures from the papers
DOI: 10.5281/zenodo.7629969
title: When to be Discrete: Analyzing Algorithm Performance on Discretized Continuous Problems - Data and Code

description: This repository contains the code and data for reproducibility of the paper 'Modular Differential Evolution'. The following files are included: - experiments: Python files which were used to create the discretized functions and collect the data (the experiment_runner files) and the code for each of the tested algorithms (two subfolders) - Process_discr: code which takes the raw IOH data and turns it into the relevant csv-files which are used to create the figures. CSV files are included 'csv_fb_*.zip'. ecdf_creation_script is used to extract ecdf and ert data for the ECDF_plot notebook. This data is added as 'other_csvs.zip' - Plot*: notebooks which are used to generate the figures from the papers
DOI: 10.6084/m9.figshare.21273105.v1
title: Direct reactions

description: Contains only direct reactions between fluorinated and defluorinated materials. In our dataset, these materials are the most promising for use as a cathode. Column titles are defined below. orig_index Original index from initial database filtering f_form Reduced formula of fluorinated structure def_form Reduced formula of defluorinated structure voltage Voltage of F/deF pair vs Li/Li+ vol_p_f Volume per mobile F (Å3) (fluorinated) per_exp Percent expansion from defluorinated to fluorinated grav_cap Gravimetric capacity (mAh/g) (fluorinated) grav_e_den Gravimetric energy density (Wh/kg) (fluorinated) vol_e_den Volumetric energy density (Wh/L) (fluorinated) f_transfer Number of mobile F ions between pair f_cost_p_kg $/kg of fluorinated structure def_cost_p_kg $/kg of defluorinated structure f_cost_p_mol $/mol of fluorinated structure def_cost_p_mol $/mol of defluorinated structure cost_p_mol_f $/mol of mobile F ion (fluorinated) log_cost_p_mol_f Log of $/mol of mobile F ion (fluorinated) f_id Materials Project ID (fluorinated) def_id Materials Project ID (defluorinated) f_spacegroup Spacegroup number (fluorinated) def_spacegroup Spacegroup number (defluorinated) f_e_hull Hull energy (eV) (fluorinated) def_e_hull Hull energy (eV) (defluorinated) f_bandgap Bandgap (eV) (fluorinated) def_bandgap Bandgap (eV) (defluorinated) f_barrier Diffusion barrier (eV) (fluorinated) def_barrier Diffusion barrier (eV) (defluorinated) direct Is conversion a direct reaction? products Products of interface reaction (equal to F structure if direct) type Complete or partial defluorination
DOI: 10.6084/m9.figshare.21273105
title: Direct reactions

description: Contains only direct reactions between fluorinated and defluorinated materials. In our dataset, these materials are the most promising for use as a cathode. Column titles are defined below. orig_index Original index from initial database filtering f_form Reduced formula of fluorinated structure def_form Reduced formula of defluorinated structure voltage Voltage of F/deF pair vs Li/Li+ vol_p_f Volume per mobile F (Å3) (fluorinated) per_exp Percent expansion from defluorinated to fluorinated grav_cap Gravimetric capacity (mAh/g) (fluorinated) grav_e_den Gravimetric energy density (Wh/kg) (fluorinated) vol_e_den Volumetric energy density (Wh/L) (fluorinated) f_transfer Number of mobile F ions between pair f_cost_p_kg $/kg of fluorinated structure def_cost_p_kg $/kg of defluorinated structure f_cost_p_mol $/mol of fluorinated structure def_cost_p_mol $/mol of defluorinated structure cost_p_mol_f $/mol of mobile F ion (fluorinated) log_cost_p_mol_f Log of $/mol of mobile F ion (fluorinated) f_id Materials Project ID (fluorinated) def_id Materials Project ID (defluorinated) f_spacegroup Spacegroup number (fluorinated) def_spacegroup Spacegroup number (defluorinated) f_e_hull Hull energy (eV) (fluorinated) def_e_hull Hull energy (eV) (defluorinated) f_bandgap Bandgap (eV) (fluorinated) def_bandgap Bandgap (eV) (defluorinated) f_barrier Diffusion barrier (eV) (fluorinated) def_barrier Diffusion barrier (eV) (defluorinated) direct Is conversion a direct reaction? products Products of interface reaction (equal to F structure if direct) type Complete or partial defluorination
DOI: 10.6084/m9.figshare.21273111
title: Direct and Indirect Reactions

description: Contains direct and indirect reactions between fluorinated and defluorinated materials. Column titles are defined below. orig_index Original index from initial database filtering f_form Reduced formula of fluorinated structure def_form Reduced formula of defluorinated structure voltage Voltage of F/deF pair vs Li/Li+ vol_p_f Volume per mobile F (Å3) (fluorinated) per_exp Percent expansion from defluorinated to fluorinated grav_cap Gravimetric capacity (mAh/g) (fluorinated) grav_e_den Gravimetric energy density (Wh/kg) (fluorinated) vol_e_den Volumetric energy density (Wh/L) (fluorinated) f_transfer Number of mobile F ions between pair f_cost_p_kg $/kg of fluorinated structure def_cost_p_kg $/kg of defluorinated structure f_cost_p_mol $/mol of fluorinated structure def_cost_p_mol $/mol of defluorinated structure cost_p_mol_f $/mol of mobile F ion (fluorinated) log_cost_p_mol_f Log of $/mol of mobile F ion (fluorinated) f_id Materials Project ID (fluorinated) def_id Materials Project ID (defluorinated) f_spacegroup Spacegroup number (fluorinated) def_spacegroup Spacegroup number (defluorinated) f_e_hull Hull energy (eV) (fluorinated) def_e_hull Hull energy (eV) (defluorinated) f_bandgap Bandgap (eV) (fluorinated) def_bandgap Bandgap (eV) (defluorinated) f_barrier Diffusion barrier (eV) (fluorinated) def_barrier Diffusion barrier (eV) (defluorinated) direct Is conversion a direct reaction? products Products of interface reaction (equal to F structure if direct) type Complete or partial defluorination
DOI: 10.6084/m9.figshare.21273111.v1
title: Direct and Indirect Reactions

description: Contains direct and indirect reactions between fluorinated and defluorinated materials. Column titles are defined below. orig_index Original index from initial database filtering f_form Reduced formula of fluorinated structure def_form Reduced formula of defluorinated structure voltage Voltage of F/deF pair vs Li/Li+ vol_p_f Volume per mobile F (Å3) (fluorinated) per_exp Percent expansion from defluorinated to fluorinated grav_cap Gravimetric capacity (mAh/g) (fluorinated) grav_e_den Gravimetric energy density (Wh/kg) (fluorinated) vol_e_den Volumetric energy density (Wh/L) (fluorinated) f_transfer Number of mobile F ions between pair f_cost_p_kg $/kg of fluorinated structure def_cost_p_kg $/kg of defluorinated structure f_cost_p_mol $/mol of fluorinated structure def_cost_p_mol $/mol of defluorinated structure cost_p_mol_f $/mol of mobile F ion (fluorinated) log_cost_p_mol_f Log of $/mol of mobile F ion (fluorinated) f_id Materials Project ID (fluorinated) def_id Materials Project ID (defluorinated) f_spacegroup Spacegroup number (fluorinated) def_spacegroup Spacegroup number (defluorinated) f_e_hull Hull energy (eV) (fluorinated) def_e_hull Hull energy (eV) (defluorinated) f_bandgap Bandgap (eV) (fluorinated) def_bandgap Bandgap (eV) (defluorinated) f_barrier Diffusion barrier (eV) (fluorinated) def_barrier Diffusion barrier (eV) (defluorinated) direct Is conversion a direct reaction? products Products of interface reaction (equal to F structure if direct) type Complete or partial defluorination
DOI: 10.6084/m9.figshare.21825210
title: Cost basis of elements

description: The cost of elements used in our analysis of material costs. The column headings are the element and cost per mass (US dollar per kg).
DOI: 10.21227/759s-8s08
title: GNSS and Physiological Data for Running Monitoring

description: Motion analysis forms a very important research topic with a general mathematical background and applications in different areas including engineering, robotics, and neurology. This paper presents the use of the global navigation satellite system (GNSS) for detection and recording of the moving body position and the simultaneous acquisition of signals from further sensors. The application is related to monitoring of physical activity and the use of wearable sensors of the heart rate and acceleration during different motion patterns. The proposed computational methodology  includes digital methods of data preprocessing, time synchronization, and data resampling to enable their correlation, feature extraction both in time and frequency domains, and classification. The datasets includes signals acquired during ten experimental runs in the selected area. Detection the motion patterns includes segmentation of signals using analysis of GNSS data, pattern evaluation, and classification of motion signals in different terrain conditions. Classification methodology provides the comparison of neural networks, support vector machine, Bayesian, and $k$-nearest neighbour methods with the highest accuracy achieved by combined features and the two layer neural network. The proposed methodology and graphical user interface suggest the efficiency of the multi-channel and multi-dimensional signal processing with applications in rehabilitation, fitness movement monitoring, neurology, engineering, and robotic systems as well.
DOI: 10.5281/zenodo.7589647
title: animacy data for animcay classification

description: This is the training data for an animacy classifier (see References LREC) 1) gold_actor: 7468 nouns denoting animate entities<br> 2) gold_nonactor 5511 nouns denoting non-animate entities subsets of 1: gold_direct 6897 nouns directly denoting animate entities<br> gold_metonym 587 metonymy trigger nouns gold_female 3738 nouns denoting female actors<br> gold_male 2830 nouns denoting male actors<br> gold_nogender 329 nouns denoting female or male actors (often plural) <br> Format: just lists Note: although some person names are in the data, a separate NER for person names should be used . References: @inproceedings{LREC,<br> month = {Juni},<br> author = {Manfred Klenner and Anne G{\"o}hring},<br> booktitle = {Proceedings of the Language Resources and Evaluation Conference},<br> address = {Marseille, France},<br> title = {Animacy Denoting {G}erman Nouns: Annotation and Classification},<br> publisher = {European Language Resources Association},<br> pages = {1360--1364},<br> year = {2022},<br> language = {english},<br> url = {https://doi.org/10.5167/uzh-219148},<br> abstract = {In this paper, we introduce a gold standard for animacy detection comprising almost 14,500 German nouns that might be used to denote either animate entities or non-animate entities. We present inter-annotator agreement of our crowd-sourced seed annotations (9,000 nouns) and discuss the results of machine learning models applied to this data.}<br> }<br>
DOI: 10.5281/zenodo.7589648
title: animacy data for animcay classification

description: This is the training data for an animacy classifier (see References LREC) 1) gold_actor: 7468 nouns denoting animate entities<br> 2) gold_nonactor 5511 nouns denoting non-animate entities subsets of 1: gold_direct 6897 nouns directly denoting animate entities<br> gold_metonym 587 metonymy trigger nouns gold_female 3738 nouns denoting female actors<br> gold_male 2830 nouns denoting male actors<br> gold_nogender 329 nouns denoting female or male actors (often plural) <br> Format: just lists Note: although some person names are in the data, a separate NER for person names should be used . References: @inproceedings{LREC,<br> month = {Juni},<br> author = {Manfred Klenner and Anne G{\"o}hring},<br> booktitle = {Proceedings of the Language Resources and Evaluation Conference},<br> address = {Marseille, France},<br> title = {Animacy Denoting {G}erman Nouns: Annotation and Classification},<br> publisher = {European Language Resources Association},<br> pages = {1360--1364},<br> year = {2022},<br> language = {english},<br> url = {https://doi.org/10.5167/uzh-219148},<br> abstract = {In this paper, we introduce a gold standard for animacy detection comprising almost 14,500 German nouns that might be used to denote either animate entities or non-animate entities. We present inter-annotator agreement of our crowd-sourced seed annotations (9,000 nouns) and discuss the results of machine learning models applied to this data.}<br> }<br>
DOI: 10.5281/zenodo.7630028
title: animacy data for animcay classification

description: This is the training data for an animacy classifier (see References LREC) 1) gold_actor: 7468 nouns denoting animate entities<br> 2) gold_nonactor 5511 nouns denoting non-animate entities subsets of 1: gold_direct 6897 nouns directly denoting animate entities<br> gold_metonym 587 metonymy trigger nouns gold_female 3738 nouns denoting female actors<br> gold_male 2830 nouns denoting male actors<br> gold_nogender 329 nouns denoting female or male actors (often plural) <br> Format: just lists Note: although some person names are in the data, a separate NER for person names should be used . References: @inproceedings{LREC,<br> month = {Juni},<br> author = {Manfred Klenner and Anne G{\"o}hring},<br> booktitle = {Proceedings of the Language Resources and Evaluation Conference},<br> address = {Marseille, France},<br> title = {Animacy Denoting {G}erman Nouns: Annotation and Classification},<br> publisher = {European Language Resources Association},<br> pages = {1360--1364},<br> year = {2022},<br> language = {english},<br> url = {https://doi.org/10.5167/uzh-219148},<br> abstract = {In this paper, we introduce a gold standard for animacy detection comprising almost 14,500 German nouns that might be used to denote either animate entities or non-animate entities. We present inter-annotator agreement of our crowd-sourced seed annotations (9,000 nouns) and discuss the results of machine learning models applied to this data.}<br> }<br>
DOI: 10.5281/zenodo.7629985
title: Surface hourly measurement data of O3, NO2 and PM2.5 for "Large modeling uncertainty in projecting decadal surface ozone changes over urban and industrial regions of China"

description: Surface hourly measurement data of O3, NO2 and PM2.5 during summer of 2017. In the .csv files, the first column contains the ID for each measurement site. "lon", "lat" are longitude and latitude, respectively. Date format is "YYYYMMDD_hour".
DOI: 10.5281/zenodo.7629984
title: Surface hourly measurement data of O3, NO2 and PM2.5 for "Large modeling uncertainty in projecting decadal surface ozone changes over urban and industrial regions of China"

description: Surface hourly measurement data of O3, NO2 and PM2.5 during summer of 2017. In the .csv files, the first column contains the ID for each measurement site. "lon", "lat" are longitude and latitude, respectively. Date format is "YYYYMMDD_hour".
DOI: 10.5281/zenodo.7629943
title: Dataset of NaOsO3 nanopillar alignment from PX I (Swiss Light Source)

description: Dataset of NaOsO3 nanopillar alignment from PX I (Swiss Light Source)
DOI: 10.5281/zenodo.7629942
title: Dataset of NaOsO3 nanopillar alignment from PX I (Swiss Light Source)

description: Dataset of NaOsO3 nanopillar alignment from PX I (Swiss Light Source)
DOI: 10.5281/zenodo.7630043
title: Animacy and non-animacy denoting nouns

description: Universität Zürich<br> Institut für Computerlinguistik<br> Manfred Klenner Die Daten sind im Rahmen eines Projekts, das vom Schweizer Nationalfond gefördert wurde (Nr. 105215-179302), entstanden.<br> ------------------------------------------------------------------------------------------------------------------------<br> **License**: Creative Commons Attribution-ShareAlike 4.0 International Public License <br> Repository: animacy data for animcay classification This is the training data for an animacy classifier (see References LREC) 1) gold_actor: 7468 nouns denoting animate entities<br> 2) gold_nonactor 5511 nouns denoting non-animate entities subsets of 1: gold_direct 6897 nouns directly denoting animate entities<br> gold_metonym 587 metonymy trigger nouns <br> Format: just lists Note: although some person names are in the data, a separate NER for person names should be used . References: @inproceedings{LREC,<br> month = {Juni},<br> author = {Manfred Klenner and Anne G{\"o}hring},<br> booktitle = {Proceedings of the Language Resources and Evaluation Conference},<br> address = {Marseille, France},<br> title = {Animacy Denoting {G}erman Nouns: Annotation and Classification},<br> publisher = {European Language Resources Association},<br> pages = {1360--1364},<br> year = {2022},<br> language = {english},<br> url = {https://doi.org/10.5167/uzh-219148},<br> abstract = {In this paper, we introduce a gold standard for animacy detection comprising almost 14,500 German nouns that might be used to denote either animate entities or non-animate entities. W<br> e present inter-annotator agreement of our crowd-sourced seed annotations (9,000 nouns) and discuss the results of machine learning models applied to this data.}<br> }<br>
DOI: 10.5281/zenodo.7630042
title: Animacy and non-animacy denoting nouns

description: Universität Zürich<br> Institut für Computerlinguistik<br> Manfred Klenner Die Daten sind im Rahmen eines Projekts, das vom Schweizer Nationalfond gefördert wurde (Nr. 105215-179302), entstanden.<br> ------------------------------------------------------------------------------------------------------------------------<br> **License**: Creative Commons Attribution-ShareAlike 4.0 International Public License <br> Repository: animacy data for animcay classification This is the training data for an animacy classifier (see References LREC) 1) gold_actor: 7468 nouns denoting animate entities<br> 2) gold_nonactor 5511 nouns denoting non-animate entities subsets of 1: gold_direct 6897 nouns directly denoting animate entities<br> gold_metonym 587 metonymy trigger nouns <br> Format: just lists Note: although some person names are in the data, a separate NER for person names should be used . References: @inproceedings{LREC,<br> month = {Juni},<br> author = {Manfred Klenner and Anne G{\"o}hring},<br> booktitle = {Proceedings of the Language Resources and Evaluation Conference},<br> address = {Marseille, France},<br> title = {Animacy Denoting {G}erman Nouns: Annotation and Classification},<br> publisher = {European Language Resources Association},<br> pages = {1360--1364},<br> year = {2022},<br> language = {english},<br> url = {https://doi.org/10.5167/uzh-219148},<br> abstract = {In this paper, we introduce a gold standard for animacy detection comprising almost 14,500 German nouns that might be used to denote either animate entities or non-animate entities. W<br> e present inter-annotator agreement of our crowd-sourced seed annotations (9,000 nouns) and discuss the results of machine learning models applied to this data.}<br> }<br>
DOI: 10.21957/wk8a-a783
title: Dropsonde data denial study for 2019 Atmospheric Rivers campaign
DOI: 10.5517/ccdc.csd.cc2d68kg
title: CCDC 2210565: Experimental Crystal Structure Determination

description: Related Article: Rex C. Handford, Trisha T. Nguyen, Simon J. Teat, R. David Britt, T. Don Tilley|2023|J.Am.Chem.Soc.|145|3031|doi:10.1021/jacs.2c11569
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.cc2dcswg
title: CCDC 2215876: Experimental Crystal Structure Determination

description: Related Article: Jessica P. Metherall, Michael R. Probert, Michael J. Hall|2022|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.