cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7611877
title: Mouse SIRV and simulated data used in the IsoQuant publication

description: This dataset contain main simulated and SIRV sequencing data used in the IsoQuant publication. - Reduced mouse GENCODE vM26 annotation: 15% of expressed isoforms removed, expression profile based on real mouse brain sequencing data; also contains GTF with expressed transcripts. - Mouse PacBio simulated data: 6 million reads generated with IsoSeqSim, 1.4% error rate. Provided as a sorted BAM file, reads were mapped to GRCm39 genome using minimap v2.18. - Mouse ONT R10.4 simulated data: 30 million read generated with NanoSim, 2.8% error rate. Provided as a sorted BAM file, reads were mapped to GRCm39 genome using minimap v2.18. - Mouse ONT R9.4 simulated data: 30 million read generated with NanoSim, 15.9% error rate. Provided as a sorted BAM file, reads were mapped to GRCm39 genome using minimap v2.18. - Lexogen SIRV Set4 sequenced on ONT MinIon with R10.4 chemistry. Provided as a sorted BAM file, reads were mapped to SIRV reference using minimap v2.18. This data was used in the IsoQuant publication. - Lexogen SIRV Set4 sequenced on ONT MinIon with R9.4 chemistry, filtered &gt;=Q12. Provided as a sorted BAM file, reads were mapped to SIRV reference using minimap v2.18.<strong> </strong>This data was NOT used in the IsoQuant publication. - SIRV reference genome and reduced annotation, also available at Lexogen download page.
DOI: 10.5281/zenodo.7121403
title: Mouse SIRV and simulated data used in the IsoQuant publication

description: This dataset contain main simulated and SIRV sequencing data used in the IsoQuant publication. - Reduced mouse GENCODE vM26 annotation: 15% of expressed isoforms removed, expression profile based on real mouse brain sequencing data; also contains GTF with expressed transcripts. - Mouse PacBio simulated data: 6 million reads generated with IsoSeqSim, 1.4% error rate. Provided as a sorted BAM file, reads were mapped to GRCm39 genome using minimap v2.18. - Mouse ONT R10.4 simulated data: 30 million read generated with NanoSim, 2.8% error rate. Provided as a sorted BAM file, reads were mapped to GRCm39 genome using minimap v2.18. - Mouse ONT R9.4 simulated data: 30 million read generated with NanoSim, 15.9% error rate. Provided as a sorted BAM file, reads were mapped to GRCm39 genome using minimap v2.18. - Lexogen SIRV Set4 sequenced on ONT MinIon with R10.4 chemistry. Provided as a sorted BAM file, reads were mapped to SIRV reference using minimap v2.18. This data was used in the IsoQuant publication. - Lexogen SIRV Set4 sequenced on ONT MinIon with R9.4 chemistry, filtered &gt;=Q12. Provided as a sorted BAM file, reads were mapped to SIRV reference using minimap v2.18.<strong> </strong>This data was NOT used in the IsoQuant publication. - SIRV reference genome and reduced annotation, also available at Lexogen download page.
DOI: 10.17632/t35xz3ntj6.1
title: Looking into the Health and Longevity of Scholars in Mathematics, Physics and Chemistry: The Case of China

description: This paper only provides the raw data of scholars in the three disciplines extracted from the official websites of 147 double first-class universities in China, which are available for life span analysis as well as to provide opinions and references for the general public to maintain their own health. The data involved in the questionnaire analysis are obtained from 210 individuals, which can be used to probe the public’s concern for health.
DOI: 10.17632/7pxmj6dnp9.1
title: Western Blots H2A-H2B recycling

description: Uncropped Western Blot Images from the submitted manuscript:

"Recycling of modified H2A-H2B provides short-term memory of chromatin states"

Authors: Valentin Flury, Nazaret Reverón-Gómez, Nicolas Alcaraz, Kathleen R. Stewart-Morgan, Alice Wenger, Robert J. Klose, Anja Groth

Overview for the conducted WB experiments
DOI: 10.5281/zenodo.7564984
title: Dataset for CLOVER PMMA experiment

description: A full experimental data set is provided. A sample made of PMMA is tested under quasi-static loading. The sample is a rectangle with 4 drilled holes. The experiment and its analysis using digital image correlation are detailed in 2 sheets. The dataset contains: - raw experimental data (load, displacement) - high resolution images - DIC data
DOI: 10.5281/zenodo.7564983
title: Dataset for CLOVER PMMA experiment

description: A full experimental data set is provided. A sample made of PMMA is tested under quasi-static loading. The sample is a rectangle with 4 drilled holes. The experiment and its analysis using digital image correlation are detailed in 2 sheets. The dataset contains: - raw experimental data (load, displacement) - high resolution images - DIC data
DOI: 10.3886/e122741
title: Knowledge of English Vocabulary

description: This is a survey experiment with answers to a test on knowledge of English vocabulary as dependent variables. Personality measures are also collected.<br>
DOI: 10.3886/e122741v2
title: Knowledge of English Vocabulary

description: This is a survey experiment with answers to a test on knowledge of English vocabulary as dependent variables. Personality measures are also collected.<br>
DOI: 10.6084/m9.figshare.21090994
title: Combined barley trials data with monthly climate data for early 20th-century Ireland

description: This early 20<sup>th</sup>-century barley trials dataset was first published by Student (1923) and consists of two barley varieties, <em>Archer </em>and <em>Goldthorpe</em>, grown from 1901-1906 across Ireland at 18 farms in total. Here we have updated the yield and price units to their modern day equivalents of tonnes per ha and (GBP) £ per ha. Latitude and longitude values are approximated from the "Place" and "District" given. Using the estimated latitude and longitude values from each farm, the nearest weather station with temperature and rainfall data available for the period 1901-1906 was identified. Recently digitised and released daily weather data from these stations was then used to estimate monthly total rainfall (mm), monthly maximum daily rainfall (mm), mean monthly maximum temperature (<sup>o</sup>C), mean monthly mean temperature (<sup>o</sup>C) and mean monthly minimum temperature (<sup>o</sup>C) at each farm.
DOI: 10.6084/m9.figshare.21090994.v1
title: Irish_barley_trials_early_20th_century.csv

description: This early 20<sup>th</sup>-century barley trials dataset was first published by Student (1923) and consists of two barley varieties, <em>Archer </em>and <em>Goldthorpe</em>, grown from 1901-1906 across Ireland at 18 farms in total. Here we have updated the yield and price units to their modern day equivalents of tonnes per ha and (GBP) £ per ha. Latitude and longitude values are approximated from the "Place" and "District" given. Using the estimated latitude and longitude values from each farm, the nearest weather station with temperature and rainfall data available for the period 1901-1906 was identified. Recently digitised and released daily weather data from these stations was then used to estimate monthly total rainfall (mm), monthly maximum daily rainfall (mm), mean monthly maximum temperature (<sup>o</sup>C), mean monthly mean temperature (<sup>o</sup>C) and mean monthly minimum temperature (<sup>o</sup>C) at each farm.
DOI: 10.23729/a049df84-20ef-4ac7-a0d2-ed9f42958125
title: Replication package for "Nonlinear nonlocal metasurfaces", Appl. Phys. Lett. 2023

description: This replication package provides detailed instructions for replicating the results presented in Figures 3 and 4 in the article "Nonlinear nonlocal metasurfaces" by R. Kolkowski, T. K. Hakala, A. Shevchenko, and M. J. Huttunen, submitted to Applied Physics Letters on 28 December 2022. The instructions are given in the PDF file "Replication instructions for Nonlinear nonlocal metasurfaces Fig 3 and 4.pdf" , which contains: * Figures 3 and 4 and their captions, as in the submitted manuscript. * Step-by-step tutorials for setting up COMSOL simulations to obtain the same results as presented in Figure 3 (eigenfrequency analysis and calculation of electric field enhancement in periodic metasurfaces). * Python codes for reading and plotting the results. * Batch submission scripts for running the computations on a cluster. * Python codes for obtaining the results presented in Figure 4 (related to temporal dynamics). In addition, this replication package contains three COMSOL model files (.mph): * Fig3a_model.mph, * Fig3b_model.mph, * Fig3c_model.mph, which can be used to obtain the results presented in Figures 3a, 3b, and 3c, respectively. These files are obtained in steps 2.26, 3.17, and 4.7 of the replication instructions. The text files: * probe_table_from_cluster.txt, * probe_table_from_cluster_2.txt, * probe_table_from_cluster_3.txt, * Emax.txt, contain the data presented in Figures 3a, 3b, 3c and 4c, respectively. They can be read and plotted using the Python codes provided in the replication instructions.
DOI: 10.14457/tu.the.2022.135
title: Effect of 4Ps marketing mix from text reviews

description: Understanding the factors contributing to customer satisfaction is important for those involved in the online trading community. The main objective of the study is to examine the factors that affect online shoppers' satisfaction. This study used text mining and content analysis from online buyer reviews that available in English only. These reviews were taken from the online shopping website (Amazon.com). This study is based on an analysis of the content of the review and focuses on helpful Web Scraper and RapidMiner demos. The data collected from Web Scraper will be sent to RapidMiner. The analysis is complete due to the selection of comments with full marks. Analysis of the content of the collected data revealed that the main factors affecting buyer satisfaction from 4Ps of marketing mix: product, price, place and promotion to categorize the data for final results.
DOI: 10.14457/tu.the.2022.137
title: Artificial intelligence and remote sensing for flood and drought detection at the parcel level in paddy fields

description: Floods and droughts cause catastrophic damage in paddy fields and create unprecedented issues for farmers and the country's economy. The victims need to be compensated for their loss based on the validation of the farmers' claims. Mobile applications have allowed farmers to claim losses by providing mobile photos and polygons of their land plots drawn on satellite base maps. With the use of remote sensing and artificial intelligence, it is possible to automate the process of detecting flood and drought disasters at a parcel level without manual inspection in each disaster-claimed field, saving money and time.The first approach is to study two deep learning-based methods that can verify these claims from the geo-tagged photographs sent by the farmers of their farms at the time of the disaster. Moreover, we demonstrate and compare the efficiency of the two methods: pixel-based semantic segmentation using DeepLabv3+ and an object-based scene recognition method using PlacesCNN. Both of the methods are powered by ResNet architecture backbones. Due to the unavailability of existing datasets for agricultural scenes, especially for the paddy farms, we prepare our own training dataset to train the Deeplabv3+ model and use an existing dataset for the PlacesCNN model. We further create a decision-based method framework that allows us to predict flood and drought from several other classes. The DeepLabv3+ and PlacesCNN-based methods achieve an accuracy of 89.09\% and 93.64\%, respectively. Our experiments show that the object-based method is superior to the pixel-based approach in terms of accuracy, data preparation, computational speed, and expense.The second approach is to use polygons provided by the farmers to access satellite images, one optical and another SAR. We study diverse methods to verify those claims at a parcel level by employing (i) Normalized Difference Vegetation Index (NDVI) and (ii) Normalized Difference Water Index (NDWI) on Sentinel-2A images, (iii) Classification and Regression Tree (CART) on Sentinel-1 SAR GRD images, and (iv) PlacesCNN on mobile photos in case the optical satellite images are disturbed by cloud coverage. To address the disturbance from clouds, we study the combination of multi-modal methods---NDVI+PlacesCNN and NDWI+PlacesCNN---that allow 86.21\% and 83.79\% accuracy in flood detection and 73.40\% and 81.91\% in drought detection, respectively. The SAR-based method outperforms the other methods in terms of accuracy in flood (98.77\%) and drought (99.44\%) detection, data acquisition, parcel coverage, cloud disturbance, and observing the area proportion of disasters in the field. The experiments conclude that the method of CART on SAR images is the most reliable to verify farmers' claims for compensation. In addition, the CNN-based method's performance on mobile photos is adequate, providing an alternative for the CART method in the case of data unavailability while using SAR images.
DOI: 10.14457/tu.the.2022.138
title: Design of a battery swapping station for multi-rotor package delivery drone

description: Drones play a major role in the fields of surveillance, package delivering, weatherforecasting, power delivering, wild-life mapping, and many other tasks also. ’Scope of themission’ and the ’Battery power’ plays an important role while using these drones. It is thatthese both issues will be caused to vary the ’Flight time’ of the drone. Instead of using humaninteraction for the battery change, battery charging techniques were implemented. Tobypass the inconvenience that happens in the battery changing process, automated batteryswapping systems were implemented. This research presents a novel concept for a batteryswapping system that uses the ‘Ceiling effect’ of the quad copter, which will stick the droneto the ceiling and swap the battery under the ceiling. The proposed ceiling-swap method allowsthe carry-under load of the delivery drone to remain attached throughout the servicingperiod. Finite Element Analysis (FEA) and a kinematic calculation were done as a simulationand the prototype was demonstrated for experiments. ’DJI TELLO’ small scale quadrotor drone was applied to do the research work and to demonstrate the proposed researchfor future directions. Finally, the advantages, limitations and the future directions of theresearch have been discussed.
DOI: 10.14457/tu.the.2022.139
title: Demand forecasting by machine learning: (case study of acrylic products)

description: In the world of business with many strong competitors, demand forecasting isone main key in the supply chain. Demand forecasting with good accuracy duringuncertainty is very challenging. Traditional forecasting is not capable when there areuncertainties. In such a case, the machine learning method is proposed to predict salesof acrylic products which is time series data through the uncertainty of the COVID-19.The last seven years’ sales data is provided by the acrylic company in Thailand. Theforecasted sale from proposed Artificial Neuron Networks method is compared withthe traditional Holt’s Double Exponential Smoothing forecasting methods bycomparing the amount of prediction error to determine the prediction accuracy. Finally,the best suitable model combination is proposed for all products properly.
DOI: 10.15479/at:ista:12102
title: Research data for "Majorana-like Coulomb spectroscopy in the absence of zero bias peaks"

description: This .zip File contains the transport data, the codes for the data analysis, the microscopy analysis and the codes for the theoretical simulations for "Majorana-like Coulomb spectroscopy in the absence of zero bias peaks" by M. Valentini, et. al. The transport data are saved with hdf5 file format. The files can be open with the log browser of Labber.
DOI: 10.6084/m9.figshare.22032575
title: Effects of aging on word position coding in Chinese reading

description: This is the data for the manuscript entitled "Effects of aging on word position coding in Chinese reading".
DOI: 10.5281/zenodo.7614722
title: Final Taxonomy for personality identification using SNS data

description: blinded_for_Review
DOI: 10.5281/zenodo.7614721
title: Final Taxonomy for personality identification using SNS data

description: blinded_for_Review
DOI: 10.5281/zenodo.7614706
title: Distribution of Rare Earth Elements and Implication for Ce anomalies in the Clay-Sized Minerals of Deep-sea Sediment, Western Pacific Ocean

description: 西太平洋深海沉积物粘土大小矿物稀土元素分布及对Ce异常的影响
DOI: 10.5281/zenodo.7614705
title: Distribution of Rare Earth Elements and Implication for Ce anomalies in the Clay-Sized Minerals of Deep-sea Sediment, Western Pacific Ocean

description: 西太平洋深海沉积物粘土大小矿物稀土元素分布及对Ce异常的影响
DOI: 10.6084/m9.figshare.22032569
title: A NEW APPROACH TO THE ESTIMATION.xlsx

description: Meteorological data is provided by Turkish State Meteorological Service free of charge to academic staff. It has h/h0 and t/t0 values used in the computation of solar radiation on a horizontal surface.
DOI: 10.6084/m9.figshare.22032569.v1
title: A NEW APPROACH TO THE ESTIMATION.xlsx

description: Meteorological data is provided by Turkish State Meteorological Service free of charge to academic staff. It has h/h0 and t/t0 values used in the computation of solar radiation on a horizontal surface.
DOI: 10.6084/m9.figshare.22032611
title: Raw data for Peer J.rar

description: Raw data for Peer J
DOI: 10.6084/m9.figshare.21836103
title: Body size, not species identity, drives body heating in alpine Erebia butterflies

description: Data accompanying the following publication: <br> Kleckova I., Okrouhlik J., Svozil T., Matos Maravi P. & Klecka J. (2022) Body size, not species identity, drives body heating in alpine <em>Erebia</em> butterflies. bioRxiv 10.1101/2022.10.03.510594. https://doi.org/10.1101/2022.10.03.510594 <br> <br> Data_body_temperature_heating.csv Raw measurements of body temperature during the heating experiments for individual butterflies. Tb = temperarture of the butterfly thorax Ta_surroundings = air temperature of the experimental room at the beginning of each measurement T_excess = the difference between Tb and Ta_surroundings air_moisture = air humidity in % measured in the experimental room at the beginning of each measurement <br> <br> Individual_level_traits_and_parameters.csv initial_Tb = body temperature of the butterfly individual at time=0 last_measured_Tb = body temperature of the butterfly at the end of the heating experiment last_measured_T_excess = the difference between Tb and Ta_surroundings at the end of the heating experiment Ts_asymptotic_body_temperature = the estimated asymptotic body temperature of the individual butterfly based on the fit of the modified Michaelis-Menten equation b_heating_rate = the estimated rate of heating of the individual butterfly based on the fit of the modified Michaelis-Menten equation Ta_surroundings = air temperature of the experimental room at the beginning of each measurement air_moisture = air humidity in % measured in the experimental room at the beginning of each measurement wing_loading = the ratio of body weight (mg) over wing area (cm^2) wing_wear = wing wear scored at the scale of 1 (wings almost perfect) to 3 (worn wings) <br> <br> <br>
DOI: 10.6084/m9.figshare.21836103.v2
title: Body size, not species identity, drives body heating in alpine Erebia butterflies

description: Data accompanying the following publication: <br> Kleckova I., Okrouhlik J., Svozil T., Matos Maravi P. & Klecka J. (2022) Body size, not species identity, drives body heating in alpine <em>Erebia</em> butterflies. bioRxiv 10.1101/2022.10.03.510594. https://doi.org/10.1101/2022.10.03.510594 <br> <br> Data_body_temperature_heating.csv Raw measurements of body temperature during the heating experiments for individual butterflies. Tb = temperarture of the butterfly thorax Ta_surroundings = air temperature of the experimental room at the beginning of each measurement T_excess = the difference between Tb and Ta_surroundings air_moisture = air humidity in % measured in the experimental room at the beginning of each measurement <br> <br> Individual_level_traits_and_parameters.csv initial_Tb = body temperature of the butterfly individual at time=0 last_measured_Tb = body temperature of the butterfly at the end of the heating experiment last_measured_T_excess = the difference between Tb and Ta_surroundings at the end of the heating experiment Ts_asymptotic_body_temperature = the estimated asymptotic body temperature of the individual butterfly based on the fit of the modified Michaelis-Menten equation b_heating_rate = the estimated rate of heating of the individual butterfly based on the fit of the modified Michaelis-Menten equation Ta_surroundings = air temperature of the experimental room at the beginning of each measurement air_moisture = air humidity in % measured in the experimental room at the beginning of each measurement wing_loading = the ratio of body weight (mg) over wing area (cm^2) wing_wear = wing wear scored at the scale of 1 (wings almost perfect) to 3 (worn wings) <br> <br> <br>
DOI: 10.6084/m9.figshare.21836103.v1
title: Thermoregulation of adult alpine butterflies: microhabitat choice is more important than physical differences

description: Data accompanying the following publication: <br> Kleckova I., Okrouhlik J., Svozil T., Matos Maravi P. & Klecka J. (2022) Thermoregulation of adult alpine butterflies: microhabitat choice is more important than physical differences. bioRxiv 10.1101/2022.10.03.510594. https://doi.org/10.1101/2022.10.03.510594 <br> <br> Data_body_temperature_heating.csv Raw measurements of body temperature during the heating experiments for individual butterflies. Tb = temperarture of the butterfly thorax Ta_surroundings = air temperature of the experimental room at the beginning of each measurement T_excess = the difference between Tb and Ta_surroundings air_moisture = air humidity in % measured in the experimental room at the beginning of each measurement <br> <br> Individual_level_traits_and_parameters.csv initial_Tb = body temperature of the butterfly individual at time=0 last_measured_Tb = body temperature of the butterfly at the end of the heating experiment last_measured_T_excess = the difference between Tb and Ta_surroundings at the end of the heating experiment Ts_asymptotic_body_temperature = the estimated asymptotic body temperature of the individual butterfly based on the fit of the modified Michaelis-Menten equation b_heating_rate = the estimated rate of heating of the individual butterfly based on the fit of the modified Michaelis-Menten equation Ta_surroundings = air temperature of the experimental room at the beginning of each measurement air_moisture = air humidity in % measured in the experimental room at the beginning of each measurement wing_loading = the ratio of body weight (mg) over wing area (cm^2) wing_wear = wing wear scored at the scale of 1 (wings almost perfect) to 3 (worn wings) <br> <br> <br>
DOI: 10.6084/m9.figshare.12287876
title: East China Sea LGM downscaling simulations

description: Surface model output and forcing (CROCO v1.0) for East China Sea LGM downscaling experiments based on PMIP3 LGM monthly climatologies. Output here is a monthly climatology calculated from the last 5 years of model output (from an 8-year total runtime), for the surface layer of the model. L1 and L2 refer to the 1/48 degree East China Sea and 1/16 degree western Pacific domains. Full methodology and description can be found in the accompanying paper.
DOI: 10.6084/m9.figshare.12287876.v3
title: East China Sea LGM downscaling simulations

description: Surface model output and forcing (CROCO v1.0) for East China Sea LGM downscaling experiments based on PMIP3 LGM monthly climatologies. Output here is a monthly climatology calculated from the last 5 years of model output (from an 8-year total runtime), for the surface layer of the model. L1 and L2 refer to the 1/48 degree East China Sea and 1/16 degree western Pacific domains. Full methodology and description can be found in the accompanying paper.