cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3889988
title: PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 18

description: These data were collected from robot controller during solar tanks welding operation.
DOI: 10.5281/zenodo.3889974
title: Solar Dataset (10 Minutes Observations)

description: The solar dataset contains approximately 6000 simulated time series representing 5-minute solar power and hourly day-ahead forecasts of photovoltaic (PV) power plants in United States in 2006. <br> The uploaded dataset contains the aggregated version of a subset of the original dataset used by Lai et al. (2017). It contains 137 time series representing the solar power production recorded per every 10 minutes in Alabama state in 2006.
description: {"references": ["National Renewable Energy Laboratory, 2020. Solar power data for integration studies. Accessed: 2020-04-30. URL https://www.nrel.gov/grid/solar-power-data.html", "Lai, G., Chang, W., Yang, Y., Liu, H., 2017. Modeling long and short-term temporal patterns with deep neural networks. CoRR abs/1703.07015.URL http://arxiv.org/abs/1703.07015", "Lai, G., 2017. multivariate-time-series-data. https://github.com/laiguokun/multivariate-time-series-data, accessed: 2020-05-04"]}
DOI: 10.5281/zenodo.3890002
title: PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 20

description: These data were collected from robot controller during solar tanks welding operation.
DOI: 10.5281/zenodo.3890001
title: PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 20

description: These data were collected from robot controller during solar tanks welding operation.
DOI: 10.5281/zenodo.3890006
title: PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 21

description: These data were collected from robot controller during solar tanks welding operation.
DOI: 10.5281/zenodo.3890005
title: PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 21

description: These data were collected from robot controller during solar tanks welding operation.
DOI: 10.5281/zenodo.3890012
title: PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 22

description: These data were collected from robot controller during solar tanks welding operation.
DOI: 10.5281/zenodo.3890011
title: PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 22

description: These data were collected from robot controller during solar tanks welding operation.
DOI: 10.5281/zenodo.3890018
title: PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 23

description: These data were collected from robot controller during solar tanks welding operation.
DOI: 10.5281/zenodo.3890017
title: PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 23

description: These data were collected from robot controller during solar tanks welding operation.
DOI: 10.5281/zenodo.3889996
title: PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 19

description: These data were collected from robot controller during solar tanks welding operation.
DOI: 10.5281/zenodo.3889995
title: PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 19

description: These data were collected from robot controller during solar tanks welding operation.
DOI: 10.5281/zenodo.3890010
title: Solar Weekly Dataset

description: The solar dataset contains approximately 6000 simulated time series representing 5-minute solar power and hourly day-ahead forecasts of photovoltaic (PV) power plants in United States in 2006. <br> The uploaded dataset contains the aggregated version of a subset of the original dataset. It contains 137 time series representing the weekly solar power production in Alabama state in 2006.
description: {"references": ["National Renewable Energy Laboratory, 2020. Solar power data for integration studies. Accessed: 2020-04-30. URL https://www.nrel.gov/grid/solar-power-data.html", "Lai, G., Chang, W., Yang, Y., Liu, H., 2017. Modeling long and short-term temporal patterns with deep neural networks. CoRR abs/1703.07015.URL http://arxiv.org/abs/1703.07015", "Lai, G., 2017. multivariate-time-series-data. https://github.com/laiguokun/multivariate-time-series-data, accessed: 2020-05-04"]}
DOI: 10.17632/33jr74ty6y.2
title: Assessment of Architectural Heritage through Bibliometric Analysis
DOI: 10.17632/33jr74ty6y.1
title: Assessment of Architectural Heritage through Bibliometric Analysis

description: This dataset is created with the help of the Web of Science database for the analysis of "Significance themes of various Assessment of Architectural Heritage through Bibliometric Analysis."
DOI: 10.17632/4tpm8whg3b.1
title: Dataset for studies on engineered hydrocortisone for treating orthotopic colon cancer
DOI: 10.17632/5s7zw4kbzp.2
title: XRD analysis of recovered iron materials from electro-coagulated sludge

description: Iron hydroxide Electro-coagulated sludge from the electrochemical processes of textile wastewater treatment plant has recovered and oxidative transformed iron oxides production with heat treatment was produced. Because hematite and magnetite iron oxides can be produce from iron hydroxide, electro-coagulated sludge as iron hydroxide and oxy-hydroxide rich sludge can be used iron oxides source for different applications. Such as for catalyst, medical and commercial applications. Heat treatment of iron hydroxide with a certain temperature range will have an effect on magnetic nature iron oxides formation. Thus, EC sludge as iron hydroxide was used crystallinity nature as well as phase identification. To this experiment, the author intends to use four critical temperature values as a base to examine the phase change by producing different iron oxides (α-Fe2O3, Fe3O4…). X-ray diffraction study using the XRD machine as well as comparative examination with standard references, the international central diffraction data (ICDD) were investigated. The heat treatment from 300 oC to 800 oC have confirmed us the iron oxide formation and at high temperature, the patterns become more crystal formation.
DOI: 10.17632/rmjnmwzmf6.1
title: PTEN and DNA-PK determine sensitivity and recovery in response to WEE1 inhibition in human breast cancer
DOI: 10.17632/r4xs5dzd9s.1
title: Scores of students in control group and experimental group under computational thinking training

description: The data includes scores of students who follow the new computational thinking training methods, containing two classes majoring in clinical from grade 2018, with 31 and 33 students each, and the scores of the whole grade following the former methods, including 249 students majoring in “5+3” clinical, anesthesiology, management and some other majors, based on the course "Medical Data Processing and Matlab Implementation".
DOI: 10.17632/kxpwmn75p7.2
title: Using Google Trends to understand population responses to the COVID-19 pandemic in Indonesia

description: This data provides information about both daily case COVID-19 case and Google RSV (Relative Search Volume) in Indonesia between January 21 - April 5. In this data we found that Google RSV for search term of category coronavirus, disease prevention, nonpharmaceutical intervention, and personal protective equiptment is strongly significant toward COVID-19 case in Indonesia (p&lt;0.05). Additionally, time lag scenario between Google RSV and COVID-19 case in Indonesia also showed good performed (r&gt;0.7; p&lt;0.05). Therefore, Google Trend is able to recognize public interest toward COVID-19 in order to reduce the transmission.
DOI: 10.17632/g2xh9k5yzy.2
title: iVR

description: iVR is a progress monitoring tool in near real-time using Digital Twin. it integrate extended reality with 3D laser scanners to exchange data between construction jobsite and construction office. the gap between office and site is reduced by utilizing the power of visual programming in tranferring and visualizing data in high quality and fast rate. the Virtual reality, Augmented reality and BIM technologies are used to make the virtual reality as close as possible to construction job site for Quality Assurance.
DOI: 10.17632/kxpwmn75p7.1
title: Using Google Trends to understand population responses to the COVID-19 pandemic in Indonesia

description: This data provides information about both daily case COVID-19 case and Google RSV (Relative Search Volume) in Indonesia between January 21 - April 5. In this data we found that Google RSV for search term of category coronavirus, disease prevention, nonpharmaceutical intervention, and personal protective equiptment is strongly significant toward COVID-19 case in Indonesia (p&lt;0.05). Additionally, time lag scenario between Google RSV and COVID-19 case in Indonesia also showed good performed (r&gt;0.7; p&lt;0.05). Therefore, Google Trend is able to recognize public interest toward COVID-19 in order to reduce the transmission.
DOI: 10.17632/bjsnjtjnzh.2
title: Exhibitors' value perceptions, exhibition attachment, and satisfaction

description: In 2019, we conducted two surveys on two landmark or famous exhibitions in Shenzhen - ICIF and Hi-tech fair. We used SPSS and Smart-pls to conduct the analysis, and the data results were in the folder.
DOI: 10.5281/zenodo.3693810
title: Broad-Coverage German Sentiment Classification Model and Dataset for Dialog Systems

description: <strong>Training a Broad-Coverage German Sentiment Classification Model for Dialog Systems</strong> This paper describes the training of a general-purpose German sentiment classification model. Sentiment classification is an important aspect of general text analytics. Furthermore, it plays a vital role in dialogue systems and voice interfaces that depend on the ability of the system to pick up and understand emotional signals from user utterances. The presented study outlines how we have collected a new German sentiment corpus and then combined this corpus with existing resources to train a broad-coverage German sentiment model. The resulting data set contains 5.4 million labelled samples. We have used the data to train both, a simple convolutional and a transformer-based classification model and compared the results achieved on various training configurations. The model and the data set will be published along with this paper. You can find the code for training testing the models, that was published along with the paper in this repository. The <em>germansentiment</em> Python package contains a easy to use interface for the model that was published with this paper.
description: This repository contains the trained models as well as the training data.
DOI: 10.5281/zenodo.3693809
title: Broad-Coverage German Sentiment Classification Model and Dataset for Dialog Systems

description: <strong>Training a Broad-Coverage German Sentiment Classification Model for Dialog Systems</strong> This paper describes the training of a general-purpose German sentiment classification model. Sentiment classification is an important aspect of general text analytics. Furthermore, it plays a vital role in dialogue systems and voice interfaces that depend on the ability of the system to pick up and understand emotional signals from user utterances. The presented study outlines how we have collected a new German sentiment corpus and then combined this corpus with existing resources to train a broad-coverage German sentiment model. The resulting data set contains 5.4 million labelled samples. We have used the data to train both, a simple convolutional and a transformer-based classification model and compared the results achieved on various training configurations. The model and the data set will be published along with this paper. You can find the code for training testing the models, that was published along with the paper in this repository. The <em>germansentiment</em> Python package contains a easy to use interface for the model that was published with this paper.
description: This repository contains the trained models as well as the training data.
DOI: 10.17632/sgnyrd5swx.1
title: Manuscript data

description: Physical activity data_202 participants
DOI: 10.17632/byxkg9z6jp.2
title: GIS-based geospatial tools for estimating the magnetic anomaly depth of hydrothermal mineral deposits using inverse distance weights method

description: The data used in this study consist of high-resolution airborne magnetic datasets for solid mineral exploration acquired across the Nigerian nationwide Terrains by the two Canadian firms awarded the contracts (a.g., Fugro Airborne Survey Services, and Patterson Grant and Watson), by the Nigerian Geological Survey Agency (NGSA). The Canadian firms had obtained the airborne magnetic data between 2003 and 2009, approximately along the NW–SE flight lines that were positioned at 90 degrees to the most significant narrow geological strike in the areas covered. Aircraft was flown at typical spaced of about half kilometers intervals to acquire the data, with a 2 km tie-line spacing along the northeast-southwest (NE-SW) directions at 80 m nominal flight elevation. The magnetometer settings were set at 0.1 s intervals to record the data. The combination of the nominal flight height—that was set exceptionally close to the ground surface using narrow line spacing—and the extremely small recording time gaps, helped to achieved a higher resolution of the magnetic anomalies than the general high-altitude airborne magnetic surveys. Prior to the data distribution by the Nigerian Geological Survey Agency, (NGSA) to the interested users, Fugro Airborne Surveys Company preprocessed the essential magnetic data corrections: the geomagnetic gradient was removed from the data using the existing model with the International Geomagnetic Reference Field (IGRF), January 2005 version, as specified in the World Geodetic System 1984 ellipsoid. The Universal Transverse Mercator (UTM) coordinate system was used to project the airborne magnetic data. The airborne magnetic survey data presented in this study covered the Omu-Aran Schist belt zone in parts of the Nigerian South-western Precambrian basement complex (NSPBC), with moderately shallow overburden lithologies.
DOI: 10.17632/byxkg9z6jp.1
title: GIS-based geospatial tools for estimating the magnetic anomaly depth of hydrothermal mineral deposits using inverse distance weights method

description: The data used in this study consist of high-resolution airborne magnetic datasets for solid mineral exploration acquired across the Nigerian nationwide Terrains by the two Canadian firms awarded the contracts (a.g., Fugro Airborne Survey Services, and Patterson Grant and Watson), by the Nigerian Geological Survey Agency (NGSA). The Canadian firms had obtained the airborne magnetic data between 2003 and 2009, approximately along the NW–SE flight lines that were positioned at 90 degrees to the most significant narrow geological strike in the areas covered. Aircraft was flown at typical spaced of about half kilometers intervals to acquire the data, with a 2 km tie-line spacing along the northeast-southwest (NE-SW) directions at 80 m nominal flight elevation. The magnetometer settings were set at 0.1 s intervals to record the data. The combination of the nominal flight height—that was set exceptionally close to the ground surface using narrow line spacing—and the extremely small recording time gaps, helped to achieved a higher resolution of the magnetic anomalies than the general high-altitude airborne magnetic surveys. Prior to the data distribution by the Nigerian Geological Survey Agency, (NGSA) to the interested users, Fugro Airborne Surveys Company preprocessed the essential magnetic data corrections: the geomagnetic gradient was removed from the data using the existing model with the International Geomagnetic Reference Field (IGRF), January 2005 version, as specified in the World Geodetic System 1984 ellipsoid. The Universal Transverse Mercator (UTM) coordinate system was used to project the airborne magnetic data. The airborne magnetic survey data presented in this study covered the Omu-Aran Schist belt zone in parts of the Nigerian South-western Precambrian basement complex (NSPBC), with moderately shallow overburden lithologies.
DOI: 10.17632/m3zjf8z286.1
title: Bubble Image Database
DOI: 10.17632/sspg4xtgnp.1
title: Pyrolysis characteristics of different occurrence organic matters

description: This data set is about the pyrolysis characteristics of soluble organic matter (SOM), clay bonded organic matter (COM) and residue particle organic matter (POM). 1-Original_pyrolysis_data is the original data of Rock_Eval VI experiments. It includes data of three kinds of sample: the raw sample; the extracted sample which is the residue of raw sample after sohxlet extraction treatment; the oxidized sample which is the residue of the extracted sample after sodium peroxodisulfate oxidation treatment. 2-Sample_weight is the recorded sample weight of each experiment. It also includes the conversion factor of the pyrolysis data of extracted sample and oxidized sample which is calculated by the sample weight. 3-Conversion_data is the converted pyrolysis data of extracted sample and oxidized sample. 4-Calculation_data is the characteristic parameters of SOM, COM and POM, which is calculated by the pyrolysis data of the raw sample, extracted sample and oxidized sample.
DOI: 10.17632/bcnv3n43wg.2
title: Classification of Microorganisms of Sukhna and Dhanas Lakes

description: The dataset consists of morphological features that map the body structure for ten different classes of microorganisms. These microorganisms are found in the lakes of Sukhna and Dhanas, Chandigarh, India. The images of microorganisms were captured by taking microscopic images of whole mounted glass slides. This dataset is used to design and construct a generic intelligent machine learning model and a pipeline of algorithms for classifying multiple microorganisms with a high level of accuracy. Following are the features extracted along with their descriptions. 1. Solidity: It is the ratio of area of an object to the area of a convex hull of the object. Computed as Area/ConvexArea. 2. Eccentricity: The eccentricity is the ratio of length of major to minor axis of an object. 3. EquivDiameter: Diameter of a circle with the same area as the region. 4. Extrema: Extrema points in the region. The format of the vector is [top-left top-right right-top right-bottom bottom-right bottom-left left-bottom left-top]. 5. Filled Area: Number of on pixels in FilledImage, returned as a scalar. 6. Extent: Ratio of the pixel area of a region with respect to the bounding box area of an object. 7. Orientation: The overall direction of the shape. The value ranges from -90 degrees to 90 degrees. 8. Euler number: Number of objects in the region minus the number of holes in those objects. 9. Bounding box: Position and size of the smallest box (rectangle) which bounds the object. 10. Convex hull: Smallest convex shape/polygon that contains the object. 11. Major axis: The major axis is the endpoints of the longest line that can be drawn through the object. Length (in pixels) of the major axis is the largest dimension of the object. 12. Minor axis: The axis perpendicular to the major axis is called the minor axis. Length (in pixels) of the minor axis is the smallest line connecting a pair of points on the contour. 13. Perimeter: Number of pixels around the border of the region. 14. Centroid: Centre of mass of the region. It is a measure of object’s location in the image. 15. Area: Total number of pixels in a region/shape.
DOI: 10.17632/tmrs92j7pv.10
title: COVID-19: Time Series Datasets India versus World

description: This dataset consists of COVID-19 time series data of India since 24th March 2020. The data set is for all the States and Union Territories of India and is divided into five parts, including i) Confirmed cases; ii) Death Count; iii) Recovered Cases; iv) Temperature of that place; and v) Percentage humidity in the region. The data set also provides basic details of confirmed cases and death count for all the countries of the world updated daily since 30 January 2020. The end user can contact the corresponding author (Rohit Salgotra : nicresearchgroup@gmail.com) for more details. . [Dataset is updated Thrice a Week]
DOI: 10.17632/37dkygd6sm.2
title: Figaro-tresses: a dataset for evaluating hair assembly features before and after cosmetic treatment (v2020)

description: The published database is composed of 1,080 images taken from 120 hair tresses made of medium blond, fine Caucasian hair with the aim to facilitate quantitative and qualitative studies about shampoo and conditioner efficacies. Two types of hair tresses were used: Caucasian hair which had not been subjected to oxidation with bleaching agents – virgin (60 tresses); and Caucasian hair, previously subjected to light oxidative bleaching – lightly bleached (remaining 60 tresses). Since cosmetic products such as shampoos and conditioners are often designed to subtly augment hair assembly features via the carefully balanced cumulative effects of deposited actives, each tress was subjected to consecutive washing+conditioning+drying cycles referred to as cosmetic treatment. The shampoo and conditioner used for this project were specifically selected for their suitability for fine hair. Each tress was photographed at three different time points: before the cosmetic treatment; after two cosmetic treatments, and after an additional third cosmetic treatment. At each timepoint, each tress was photographed from three different angles (-45, 0, and +45 degrees), resulting in a total number of nine images for each tress. For each image in the database, we also provide a corresponding hair segmentation mask, which identifies the hair location area in the original image.
DOI: 10.5281/zenodo.3890030
title: Sunspot Daily Dataset (with Missing Values)

description: This dataset contains a single very long daily time series of sunspot numbers from 1818-01-01 to 2020-05-31.
description: {"references": ["Sunspot, 2015. Sunspot number version 2.0: new data and conventions. URL http://www.sidc.be/silso/newdataset"]}
DOI: 10.15156/bio/sh0784159.10fu
title: SH0784159.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.