cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4923132
title: Experimental raw data for "Characterization of activated carbons for water treatment using TGA-FTIR for analysis of oxygen-containing functional groups"

description: LICENSE: These data are made available under the Public Domain Dedication and License v1.0 whose full text can be found at: http://opendatacommons.org/licenses/pddl/1.0/ <br> This is the repository of all experimental raw data used in the Applied Water Science publication "Characterization of activated carbons for water treatment using TGA-FTIR for analysis of oxygen-containing functional groups" by Daniel Dittmann, Leon Saal, Frederik Zietzschmann, Maike Mai, Korinna Altmann, Dominik Al-Sabbagh, Pia Schumann, Aki Sebastian Ruhl, Martin Jekel, and Ulrike Braun. It includes<br> - Results_summary (.xlsx and .ods) containing overview tables with all numerical results for the eight selected PACs<br> - 01_N2_Adsorption_Desorption<br> - 02_Particle_Counter<br> - 03_CHNS<br> - 04_PZC<br> - 05_XPS<br> - 06_XRF<br> - 07_XRD<br> - 08_TGA-FTIR<br> - 09_PCA_all_properties<br> - 10_OMP_removals Subfolders are provided for<br> - "Raw_Data"<br> - "processed_for_plotting" with Origin files<br> - "easyPCA" with data for principal component analyses using Jupyter Notesbooks from https://github.com/danield5732/easyPCA
description: {"references": ["Dittmann et al. (2022), https://doi.org/10.1007/s13201-022-01723-2"]}
DOI: 10.5281/zenodo.4923133
title: Experimental raw data for "Characterization of activated carbons for water treatment using TGA-FTIR for analysis of oxygen-containing functional groups"

description: LICENSE: These data are made available under the Public Domain Dedication and License v1.0 whose full text can be found at: http://opendatacommons.org/licenses/pddl/1.0/ <br> This is the repository of all experimental raw data used in the Applied Water Science publication "Characterization of activated carbons for water treatment using TGA-FTIR for analysis of oxygen-containing functional groups" by Daniel Dittmann, Leon Saal, Frederik Zietzschmann, Maike Mai, Korinna Altmann, Dominik Al-Sabbagh, Pia Schumann, Aki Sebastian Ruhl, Martin Jekel, and Ulrike Braun. It includes<br> - Results_summary (.xlsx and .ods) containing overview tables with all numerical results for the eight selected PACs<br> - 01_N2_Adsorption_Desorption<br> - 02_Particle_Counter<br> - 03_CHNS<br> - 04_PZC<br> - 05_XPS<br> - 06_XRF<br> - 07_XRD<br> - 08_TGA-FTIR<br> - 09_PCA_all_properties<br> - 10_OMP_removals Subfolders are provided for<br> - "Raw_Data"<br> - "processed_for_plotting" with Origin files<br> - "easyPCA" with data for principal component analyses using Jupyter Notesbooks from https://github.com/danield5732/easyPCA
description: {"references": ["Dittmann et al. (2022), https://doi.org/10.1007/s13201-022-01723-2"]}
DOI: 10.5281/zenodo.3973285
title: Dagstuhl-15512-ArgQuality

description: An English corpus for studying the assessment of argumentation quality. It contains 320 online debate portal arguments, annotated for 15 different quality dimensions by three annotators. In version 2, the annotated XMI files have been changed according to a new underlying type system where each quality dimension is represented by an own annotation. This annotation contains not only the majority score of the respective dimension (as in version 1), but also the mean score and the scores of all annotators. We recommend to use version 2. In case you publish any results related to the Dagstuhl-15512 ArgQuality corpus, please cite our EACL 2017 paper on argumentation quality.
DOI: 10.5281/zenodo.3973284
title: Dagstuhl-15512-ArgQuality

description: An English corpus for studying the assessment of argumentation quality. It contains 320 online debate portal arguments, annotated for 15 different quality dimensions by three annotators. In version 2, the annotated XMI files have been changed according to a new underlying type system where each quality dimension is represented by an own annotation. This annotation contains not only the majority score of the respective dimension (as in version 1), but also the mean score and the scores of all annotators. We recommend to use version 2. In case you publish any results related to the Dagstuhl-15512 ArgQuality corpus, please cite our EACL 2017 paper on argumentation quality.
DOI: 10.6084/m9.figshare.20705728.v1
title: Larval and silk gland weight of 5th instar Bombyx mori larvae

description: The above data shows the weight of larvae and silk gland of day 5 of 5th instar larval stage of <em>Bombyx. mori</em> after heat shock with different temperatures.
DOI: 10.5281/zenodo.3715260
title: Experimental raw data for "Adsorbed carbamazepine on different activated carbons is analysed by thermal decomposition deriving specific adsorption sites and conditions"

description: LICENSE: These data are made available under the Public Domain Dedication and License v1.0 whose full text can be found at: http://opendatacommons.org/licenses/pddl/1.0/ <br> This is the repository of all experimental raw data used in the Scientific Reports publication "Adsorbed carbamazepine on different activated carbons is analysed by thermal decomposition deriving specific adsorption sites and conditions" by Daniel Dittmann, Paul Eisentraut, Caroline Goedecke, Yosri Wiesner, Martin Jekel, Aki Sebastian Ruhl, and Ulrike Braun. It includes<br> - overview_measurements.xlsx and overview_measurements.ods containing a list of all TGA experiments (TGA, TGA-FTIR, TED-GC-MS, and ramp-kinetics)<br> - TED-GC-MS.zip containing gas chromatography-mass spectrometry experimtent files for the Chemstation and OpenChrom<br> - TGA.zip containing thermogravimetric analyses raw data on evolved gas analyses experiments (TGA-FTIR and TED-GC-MS)<br> - TGA_kinetics.zip containing thermogravimetric analyses raw data on decomposition kinetic experiments (ramp-kinetics)<br> - TGA-FTIR.zip containing Fourier-transform infrared spectroscopy series files for OMNIC<br> - XRF.zip containing x-ray flourescence data on elemental composition
DOI: 10.5281/zenodo.3716316
title: Experimental raw data for "Specific adsorption sites and conditions derived by thermal decomposition of activated carbons and adsorbed carbamazepine"

description: LICENSE: These data are made available under the Public Domain Dedication and License v1.0 whose full text can be found at: http://opendatacommons.org/licenses/pddl/1.0/ <br> This is the repository of all experimental raw data used in the Scientific Reports publication "Specific adsorption sites and conditions derived by thermal decomposition of activated carbons and adsorbed carbamazepine" by Daniel Dittmann, Paul Eisentraut, Caroline Goedecke, Yosri Wiesner, Martin Jekel, Aki Sebastian Ruhl, and Ulrike Braun. It includes<br> - overview_measurements.xlsx and overview_measurements.ods containing a list of all TGA experiments (TGA, TGA-FTIR, TED-GC-MS, and ramp-kinetics)<br> - TED-GC-MS.zip containing gas chromatography-mass spectrometry experimtent files for the Chemstation and OpenChrom<br> - TGA.zip containing thermogravimetric analyses raw data on evolved gas analyses experiments (TGA-FTIR and TED-GC-MS)<br> - TGA_kinetics.zip containing thermogravimetric analyses raw data on decomposition kinetic experiments (ramp-kinetics)<br> - TGA-FTIR.zip containing Fourier-transform infrared spectroscopy series files for OMNIC<br> - XRF.zip containing x-ray flourescence data on elemental composition
DOI: 10.5281/zenodo.3275474
title: Experimental raw data for "Specific adsorption sites and conditions derived by thermal decomposition of activated carbons and adsorbed carbamazepine"

description: LICENSE: These data are made available under the Public Domain Dedication and License v1.0 whose full text can be found at: http://opendatacommons.org/licenses/pddl/1.0/ <br> This is the repository of all experimental raw data used in the Scientific Reports publication "Specific adsorption sites and conditions derived by thermal decomposition of activated carbons and adsorbed carbamazepine" by Daniel Dittmann, Paul Eisentraut, Caroline Goedecke, Yosri Wiesner, Martin Jekel, Aki Sebastian Ruhl, and Ulrike Braun. It includes<br> - overview_measurements.xlsx and overview_measurements.ods containing a list of all TGA experiments (TGA, TGA-FTIR, TED-GC-MS, and ramp-kinetics)<br> - TED-GC-MS.zip containing gas chromatography-mass spectrometry experimtent files for the Chemstation and OpenChrom<br> - TGA.zip containing thermogravimetric analyses raw data on evolved gas analyses experiments (TGA-FTIR and TED-GC-MS)<br> - TGA_kinetics.zip containing thermogravimetric analyses raw data on decomposition kinetic experiments (ramp-kinetics)<br> - TGA-FTIR.zip containing Fourier-transform infrared spectroscopy series files for OMNIC<br> - XRF.zip containing x-ray flourescence data on elemental composition
DOI: 10.6084/m9.figshare.20448234
title: GNSS Data for “Quantitative Evaluation of Environmental Loading Products and Thermal Expansion Effect for Correcting GNSS Vertical Coordinate Time Series in Taiwan ”

description: Data for “Quantitative Evaluation of Environmental Loading Products and Thermal Expansion Effect for Correcting GNSS Vertical Coordinate Time Series in Taiwan ”
DOI: 10.6084/m9.figshare.20448234.v2
title: GNSS Data for “Quantitative Evaluation of Environmental Loading Products and Thermal Expansion Effect for Correcting GNSS Vertical Coordinate Time Series in Taiwan ”

description: Data for “Quantitative Evaluation of Environmental Loading Products and Thermal Expansion Effect for Correcting GNSS Vertical Coordinate Time Series in Taiwan ”
DOI: 10.15151/esrf-es-886831222
title: In-situ crystallisation of sodium sulfate in confinement

description: Crystallisation in porous media is an important process in many natural systems, including biomineralisation, frost heave and salt weathering. Due to the inherent difficulty to study these processes in-situ, many questions regarding the evolution of crystals within confinement remain. We propose to investigate the crystallisation of sodium sulfate within controlled pore glass rods composed of a network of 7 nm sized pores. Using in-situ X-ray computed tomography and diffraction contrast tomography we will monitor the evolution - polymorphism and morphology - of individual particles over time. Surface chemistry will be altered in order to investigate its role in polymorph selection. Additionally, we will aim to describe the process of material damage due to crystallisation pressure due to growth and polymorph transition. This work will enhance our understanding of crystallisation in confinement and unveil the precursor activity leading to material damage due to crystallisation.
DOI: 10.5281/zenodo.7030756
title: Learner evaluation forms NextFOOD

description: This dataset entails qualitative data on students’ experiences of participating in the FooFactory-Us competition before, during and after completion.
DOI: 10.5281/zenodo.7030757
title: Learner evaluation forms NextFOOD

description: This dataset entails qualitative data on students’ experiences of participating in the FooFactory-Us competition before, during and after completion.
DOI: 10.25375/uct.20682157
title: Input data for the 2013 rock lobster assessments of the Tristan da Cunha group of islands

description: The following data are used as model inputs to the 2013 updated assessments (Johnston and Butterworth 2013) of the Tristan da Cunha rock lobsters. Note the convention that, for example, the 2007 season refers to the period Sept 2007 to Aug 2008.
DOI: 10.25375/uct.20682157.v1
title: Input data for the 2013 rock lobster assessments of the Tristan da Cunha group of islands

description: The following data are used as model inputs to the 2013 updated assessments (Johnston and Butterworth 2013) of the Tristan da Cunha rock lobsters. Note the convention that, for example, the 2007 season refers to the period Sept 2007 to Aug 2008.
DOI: 10.25578/gpbnkb
title: Re-routing track based on an avoidance map in Lyon Metropolis

description: The dataset consists of users'trips invited to follow a route that encourages them to avoid urban areas where traffic conditions become too congested. A navigation session is made of a GPS track (i.e. a sequence of (latitude, longitude) coordinates) and the corresponding timestamps sequence.
DOI: 10.25578/gpbnkb/ch1dnd
title: Readme_ReRoutingDataset.pdf

description: description of data
DOI: 10.25578/gpbnkb/eh5gup
title: Dataset_ReRoutingTrack.txt

description: 668 sessions; 11 variables; format: txt with separator "\t"
DOI: 10.25646/10136
title: Tagesdaten-CSV aus dem DIVI-Intensivregister

description: Die Tagesdaten-CSV entspricht einem Auszug der Daten des DIVI-Intensivregisters. Die Datei enthält eine Aggregation der aktuellsten Meldungen pro Landkreis. Es werden die aktuell gemeldeten Anzahlen der COVID-19 Intensivfälle sowie die gemeldeten intensivmedizinischen Behandlungskapazitäten angezeigt. Die Tagesdaten-CSV liefert dabei ausschließlich einen Blick auf die Daten gemäß dem Stand des betrachteten Tages. Die Daten sind im situationsbedingten Kontext aufbereitet, damit sind verschiedene Tagesdaten-CSVs u.U. nicht direkt miteinander vergleichbar. Die aktuellsten Meldungen werden im gewählten Betrachtungszeitfenster über alle Meldebereiche und Standorte aufsummiert. Weitere Informationen sind zu finden unter https://www.intensivregister.de/#/faq
DOI: 10.25646/10141
title: Tagesdaten-CSV aus dem DIVI-Intensivregister

description: Die Tagesdaten-CSV entspricht einem Auszug der Daten des DIVI-Intensivregisters. Die Datei enthält eine Aggregation der aktuellsten Meldungen pro Landkreis. Es werden die aktuell gemeldeten Anzahlen der COVID-19 Intensivfälle sowie die gemeldeten intensivmedizinischen Behandlungskapazitäten angezeigt. Die Tagesdaten-CSV liefert dabei ausschließlich einen Blick auf die Daten gemäß dem Stand des betrachteten Tages. Die Daten sind im situationsbedingten Kontext aufbereitet, damit sind verschiedene Tagesdaten-CSVs u.U. nicht direkt miteinander vergleichbar. Die aktuellsten Meldungen werden im gewählten Betrachtungszeitfenster über alle Meldebereiche und Standorte aufsummiert. Weitere Informationen sind zu finden unter https://www.intensivregister.de/#/faq
DOI: 10.21227/dxef-tx53
title: Feature Representation Repository for Defect Prediction

description: The software engineering community is working to develop reliable metrics to improve software quality. It is estimated that understanding the source code accounts for 60% of the software maintenance effort. Cognitive informatics is important in quantifying the degree of difficulty or the efforts made by developers to understand the source code. Several empirical studies were conducted in 2003 to assign cognitive weights to each possible basic control structure of software, and these cognitive weights are used by several researchers to evaluate the cognitive complexity of software systems. In this paper, an effort has been made to categorize the Control Flow Graphs (CFGs) nodes according to their node features. In our case, we extracted seven unique features from the program, and each unique feature was assigned an integer value that we evaluated through Cognitive Complexity Measures (CCMs). We then incorporated CCMs' results as a node feature value in CFGs and generated the same based on the node connectivity for a graph. In order to obtain the feature representation of the graph, a node vector matrix is then created for the graph and passed to the Graph Convolutional Network (GCN). We prepared our data sets using GCN output and then built Deep Neural Network Defect Prediction (DNN-DP) and Convolutional Neural Network Defect Prediction (CNN-DP) models to predict software defects. The Python programming language is used, along with Keras and TensorFlow. Three hundred twenty Python programs were written by our talented UG and PG students, and all experiments were carried out during laboratory classes. Together with three skilled lab programmers, they compiled and ran each individual program and detected defect/no-defect programs before categorizing them into three different classes, namely Simple, Medium, and Complex programs. Accuracy, Receiver Operating Characteristics (ROC), Area Under Curve (AUC), F-measure, Precision and hyper-parameter tuning procedures are used to evaluate the approaches. The experimental results show that the proposed models outperformed state-of-the-art methods such as Nave Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF) in all evaluation criteria.
DOI: 10.5281/zenodo.7026720
title: Long et al. (2022) JGR submission - shale

description: Long et al. (2022) JGR submission shale measurement data
DOI: 10.5281/zenodo.7026719
title: Long et al. (2022) JGR submission - shale

description: Long et al. (2022) JGR submission shale measurement data
DOI: 10.5281/zenodo.6579813
title: Nesting Tasks Dataset for 2D-Nesting Efficiency Estimation

description: Nesting efficiency dataset This is the raw dataset associated with the paper “Graph Neural Networks Comparison for 2D-Nesting Efficiency Estimation”, by C.Lallier, L. Vézard, B. Pinaud and G. Blin, 2022. Consisting of 100,000 nesting tasks. <strong>Usage:</strong> The files are: <em>tasks.gz, parts.gz, constraints.gz, </em>and<em> shapes.gz</em>. They are in PICKLE file format version 5 with a gzip compression. Example to load a file : <pre><code class="language-python">import pandas as pd tasks = pd.read_pickle('tasks.gz')</code></pre> <strong>Description:</strong> <em>Tasks.gz</em> file contains nestings high-level descriptors. It is composed of the following columns: Column Type Description efficiency float The variable to predict (label). Given in % duration integer input data. The nesting algorithm convergence time. Given in s. sheet_width integer input data. The width of the nesting area. Given in m<sup>-4</sup> sheet_length integer input data. Facultative. The height of the nesting area. Given in m<sup>-4</sup> sheet_type integer input data. Kind of the nesting. tasks_index integer Generated data. Join key between tables. is_train, is_val, is_test boolean Generated data. Can be used as mask for the train, val and test subsets. <em>Parts.gz</em> contains description of the parts to be nested : Column Type Description <strong>tasks_index</strong> integer Reference to the join key from the <em>Task</em> table. <strong>parts_id</strong> integer Generated part id. <strong>shape_hash</strong> integer Reference to the hash of the part's shape, join key from the <em>Shape </em>table. <em>Shapes.gz</em> is the description of the shapes of the parts to be nested : Column Type Description <strong>shape_hash</strong> integer Generated data. Join key between tables. <strong>raw</strong> list of integers List of x, y tuples for each point. Unit is m<sup>-4</sup> <strong>sizes</strong> list of integers List of sub-shapes sizes. <em>Constraints.gz</em> describes constraints and their parameters: Column Type Description <strong>type</strong> string Generated constraint type. <strong>tasks_index</strong> integer Reference to the join key from the <em>Task</em> table. <strong>parts_1, parts_2</strong> list of integers References to the <em>parts_id</em> from the <em>Parts</em> table. <strong>p1_x, p1_y and p2_x, p2_y</strong> list of floats Input data. Origin position (x, y) of the constraint on parts. For each part of the constraint. <strong>r1_start, r1_end, r1_flip_x</strong> list of floats Input data. Rotation (start, end, and flip_x) parameters of the constraint. Multiple ranges accepted. <strong>y_min, y_max</strong> list of floats Input data. Range from (y_min, y_max). Multiple ranges accepted. <strong>x_offset, y_offset, motif_order, x_alignment_type, y_alignment_type, proximity_type, max_distance, groups_relative_orientation, is_frozen</strong> float Input data. Other constraint parameters.
description: Fix an error in 'constraints.gz', swap 'c8' and 'g4' values in column 'type'
DOI: 10.5281/zenodo.6610253
title: Nesting Tasks Dataset for 2D-Nesting Efficiency Estimation

description: Nesting efficiency dataset This is the raw dataset associated with the paper “Graph Neural Networks Comparison for 2D-Nesting Efficiency Estimation”, by C.Lallier, L. Vézard, B. Pinaud and G. Blin, 2022. Consisting of 100,000 nesting tasks. <strong>Usage:</strong> The files are: <em>tasks.gz, parts.gz, constraints.gz, </em>and<em> shapes.gz</em>. They are in PICKLE file format version 5 with a gzip compression. Example to load a file : <pre><code class="language-python">import pandas as pd tasks = pd.read_pickle('tasks.gz')</code></pre> <strong>Description:</strong> <em>Tasks.gz</em> file contains nestings high-level descriptors. It is composed of the following columns: Column Type Description efficiency float The variable to predict (label). Given in % duration integer input data. The nesting algorithm convergence time. Given in s. sheet_width integer input data. The width of the nesting area. Given in m<sup>-4</sup> sheet_length integer input data. Facultative. The height of the nesting area. Given in m<sup>-4</sup> sheet_type integer input data. Kind of the nesting. tasks_index integer Generated data. Join key between tables. is_train, is_val, is_test boolean Generated data. Can be used as mask for the train, val and test subsets. <em>Parts.gz</em> contains description of the parts to be nested : Column Type Description <strong>tasks_index</strong> integer Reference to the join key from the <em>Task</em> table. <strong>parts_id</strong> integer Generated part id. <strong>shape_hash</strong> integer Reference to the hash of the part's shape, join key from the <em>Shape </em>table. <em>Shapes.gz</em> is the description of the shapes of the parts to be nested : Column Type Description <strong>shape_hash</strong> integer Generated data. Join key between tables. <strong>raw</strong> list of integers List of x, y tuples for each point. Unit is m<sup>-4</sup> <strong>sizes</strong> list of integers List of sub-shapes sizes. <em>Constraints.gz</em> describes constraints and their parameters: Column Type Description <strong>type</strong> string Generated constraint type. <strong>tasks_index</strong> integer Reference to the join key from the <em>Task</em> table. <strong>parts_1, parts_2</strong> list of integers References to the <em>parts_id</em> from the <em>Parts</em> table. <strong>p1_x, p1_y and p2_x, p2_y</strong> list of floats Input data. Origin position (x, y) of the constraint on parts. For each part of the constraint. <strong>r1_start, r1_end, r1_flip_x</strong> list of floats Input data. Rotation (start, end, and flip_x) parameters of the constraint. Multiple ranges accepted. <strong>y_min, y_max</strong> list of floats Input data. Range from (y_min, y_max). Multiple ranges accepted. <strong>x_offset, y_offset, motif_order, x_alignment_type, y_alignment_type, proximity_type, max_distance, groups_relative_orientation, is_frozen</strong> float Input data. Other constraint parameters.