cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/vteb-j724
title: Dataset for Disentangled Representation Learning for Interpretable Molecule Generation

description: Expanding our knowledge of small molecules beyond what is known in nature or designed in wet laboratories promises to significantly advance drug discovery, biotechnology, and material science. Computing novel small molecules with specific structural and functional properties is non-trivial, primarily due to the size, dimensionality, and multi-modality of the corresponding search space. Deep generative models that learn directly from data without the need for domain insight are recently providing a way forward. In particular, graph generative frameworks, which are able to capture detailed atomic interactions via their graph-based representation of a small molecule, are showing promising results. However, these frameworks remain opaque and do not allow obtaining any insight into their generation process. In this paper we present a first step towards addressing this limitation by leveraging the concept of disentanglement in the graph variational autoencoder framework. {\color{blue}We propose various disentanglement learning techniques within this framework, resulting in novel disentangled deep graph generative models which we compare against the state of the art in graph generative deeep learning for small molecule generation. We demonstrate that the models achieve the learning objective for inference and generation for variable-size graphs efficiently.} Extensive qualitative and quantitative experimental evaluation demonstrates the superiority of our disentanglement framework for small molecule generation along various critical measures, such as accuracy, novelty, and disentanglement learning.
DOI: 10.5281/zenodo.4699319
title: Land cover datasets of Ramsar wetland sites in China

description: Land cover dataset (1978, 1990, 2000, 2010, 2018) with 30 m resolution obtained from Landsat images using a hybrid approach combining object-based image analysis and hierarchical decision-tree classification (HOHC).
description: {"references": ["Mao et al., 2020. National wetland mapping in China: a new product resulting from object-based and hierarchical classification of Landsat 8 OLI images. ISPRS Journal of Photogrammetry and Remote Sensing, 164, 11\u201325."]}
DOI: 10.5281/zenodo.4699318
title: Land cover datasets of Ramsar wetland sites in China

description: Land cover dataset (1978, 1990, 2000, 2010, 2018) with 30 m resolution obtained from Landsat images using a hybrid approach combining object-based image analysis and hierarchical decision-tree classification (HOHC).
description: {"references": ["Mao et al., 2020. National wetland mapping in China: a new product resulting from object-based and hierarchical classification of Landsat 8 OLI images. ISPRS Journal of Photogrammetry and Remote Sensing, 164, 11\u201325."]}
DOI: 10.6084/m9.figshare.12789902
title: Raw Data for FDAAA 2007 Analysis

description: This is the underlying raw data for our FDAAA analysis code available at:<br>https://github.com/ebmdatalab/fdaaa_requirements<br>
DOI: 10.6084/m9.figshare.12789902.v1
title: Raw Data for FDAAA 2007 Analysis

description: This is the underlying raw data for our FDAAA analysis code available at:<br>https://github.com/ebmdatalab/fdaaa_requirements<br>
DOI: 10.6084/m9.figshare.14370272.v1
title: MarsWRF_data

description: Simulation of Martian dust effects on the polar CO<sub>2</sub> ice caps and atmospheric circulation using the MarsWRF model
DOI: 10.13012/b2idb-0299659_v1
title: Multi-scale CyberGIS Analytics for Detecting Spatiotemporal Patterns of COVID-19

description: This dataset contains all the code, notebooks, datasets used in the study conducted for the research publication titled "Multi-scale CyberGIS Analytics for Detecting Spatiotemporal Patterns of COVID-19 Data". Specifically, this package include the artifacts used to conduct spatial-temporal analysis with space time kernel density estimation (STKDE) using COVID-19 data, which should help readers to reproduce some of the analysis and learn about the methods that were conducted in the associated book chapter. ## What’s inside - A quick explanation of the components of the zip file * Multi-scale CyberGIS Analytics for Detecting Spatiotemporal Patterns of COVID-19.ipynb is a jupyter notebook for this project. It contains codes for preprocessing, space time kernel density estimation, postprocessing, and visualization. * data is a folder containing all data needed for the notebook * data/county.txt: US counties information and fip code from Natural Resources Conservation Service. * data/us-counties.txt: County-level COVID-19 data collected from New York Times COVID-19 github repository on August 9th, 2020. * data/covid_death.txt: COVID-19 death information derived after preprocessing step, preparing the input data for STKDE. Each record is if the following format (fips, spatial_x, spatial_y, date, number of death ). * data/stkdefinal.txt: result obtained by conducting STKDE. * wolfram_mathmatica is a folder for 3D visulization code. * wolfram_mathmatica/Visualization.nb: code for visulization of STKDE result via weolfram mathmatica. * img is a folder for figures. * img/above.png: result of 3-D visulization result, above view. * img/side.png: result of 3-D visulization, side view.
DOI: 10.6084/m9.figshare.14445399
title: Kitap1.xlsx

description: doi numarası almak için deneme
DOI: 10.6084/m9.figshare.14445399.v1
title: Kitap1.xlsx

description: doi numarası almak için deneme
DOI: 10.5281/zenodo.4699429
title: Supplementary material of RNAmining software

description: Supplementary material containing informations about the RNAmining software publication.
DOI: 10.5281/zenodo.4699428
title: Supplementary material of RNAmining software

description: Supplementary material containing informations about the RNAmining software publication.
DOI: 10.5281/zenodo.4697303
title: Figure 10 from article Crystals Cadmium Telluride Nanocomposite Films Formation from Thermal Decomposition of Cadmium Carboxylate Precursor and Their Photoluminescence Shift from Green to Red

description: The dataset includes two files: the word file describes the type of sample and the procedures used to pick up the data; the excel file includes the raw data used to obtain the plots of figure xy. In the following is reported the description of figure xy as wrote in the paper: Normalized absorption spectra of the CdTe QDs prepared at 150 °C after 5 (a) and 30 (b) minutes of annealing in solution (dashed line) and after encapsulation in the polymer (solid line). Normalized PL spectra of the CdTe QDs prepared after 5 (c) and 30 (d) minutes of annealing in solution (dashed line) and after encapsulation in the polymer (solid line). Inset: photographs of the films acquired under the UV lamp at 365 nm.
DOI: 10.5281/zenodo.4697302
title: Figure 10 from article Crystals Cadmium Telluride Nanocomposite Films Formation from Thermal Decomposition of Cadmium Carboxylate Precursor and Their Photoluminescence Shift from Green to Red

description: The dataset includes two files: the word file describes the type of sample and the procedures used to pick up the data; the excel file includes the raw data used to obtain the plots of figure xy. In the following is reported the description of figure xy as wrote in the paper: Normalized absorption spectra of the CdTe QDs prepared at 150 °C after 5 (a) and 30 (b) minutes of annealing in solution (dashed line) and after encapsulation in the polymer (solid line). Normalized PL spectra of the CdTe QDs prepared after 5 (c) and 30 (d) minutes of annealing in solution (dashed line) and after encapsulation in the polymer (solid line). Inset: photographs of the films acquired under the UV lamp at 365 nm.
DOI: 10.5281/zenodo.4697282
title: Figure 8 from article Crystals "Cadmium Telluride Nanocomposite Films Formation from Thermal Decomposition of Cadmium Carboxylate Precursor and Their Photoluminescence Shift from Green to Red

description: The dataset includes two files: the word file describes the type of sample and the procedures used to pick up the data; the excel file includes the raw data used to obtain the plots of figure 8. In the following is reported the description of figure 8 as wrote in the paper: PL peak position as a function of CdTe QDs size grown in solution (the linear fit is reported as a solid line)
DOI: 10.5281/zenodo.4697281
title: Figure 8 from article Crystals "Cadmium Telluride Nanocomposite Films Formation from Thermal Decomposition of Cadmium Carboxylate Precursor and Their Photoluminescence Shift from Green to Red

description: The dataset includes two files: the word file describes the type of sample and the procedures used to pick up the data; the excel file includes the raw data used to obtain the plots of figure 8. In the following is reported the description of figure 8 as wrote in the paper: PL peak position as a function of CdTe QDs size grown in solution (the linear fit is reported as a solid line)
DOI: 10.5281/zenodo.4697309
title: Figure 7 from article Crystals "Cadmium Telluride Nanocomposite Films Formation from Thermal Decomposition of Cadmium Carboxylate Precursor and Their Photoluminescence Shift from Green to Red

description: The dataset includes two files: the word file describes the type of sample and the procedures used to pick up the data; the excel file includes the raw data used to obtain the plots of figure 7. In the following is reported the description of figure 7 as wrote in the paper. Calculated size of CdTe QDs versus time course both in solid state (red line same curve of Figure 3) and in solution (black line).
DOI: 10.5281/zenodo.4697308
title: Figure 7 from article Crystals "Cadmium Telluride Nanocomposite Films Formation from Thermal Decomposition of Cadmium Carboxylate Precursor and Their Photoluminescence Shift from Green to Red

description: The dataset includes two files: the word file describes the type of sample and the procedures used to pick up the data; the excel file includes the raw data used to obtain the plots of figure 7. In the following is reported the description of figure 7 as wrote in the paper. Calculated size of CdTe QDs versus time course both in solid state (red line same curve of Figure 3) and in solution (black line).
DOI: 10.5281/zenodo.4699458
title: Meiofauna Raw Data

description: Data set of meiofauna, and environmental variables collected in Prieta in the year 2017.
DOI: 10.5281/zenodo.3879232
title: Meiofauna Raw Data

description: Data set of meiofauna, and environmental variables collected in Prieta in the year 2017.
DOI: 10.5281/zenodo.4604619
title: Axion-Photon Conversion in Magnetospheres: The Role of the Plasma in the GJ Model

description: This contains data generated from a ray tracing algorithm intended to track the trajectories of radio photons, sourced from the resonant conversion of axion dark matter, as they exit the magnetosphere of neutron stars. See paper for details. Plotting functions available at https://github.com/SamWitte/Anteater_DataShare.
DOI: 10.5281/zenodo.4604618
title: Axion-Photon Conversion in Magnetospheres: The Role of the Plasma in the GJ Model

description: This contains data generated from a ray tracing algorithm intended to track the trajectories of radio photons, sourced from the resonant conversion of axion dark matter, as they exit the magnetosphere of neutron stars. See paper for details. Plotting functions available at https://github.com/SamWitte/Anteater_DataShare.
DOI: 10.34740/kaggle/dsv/2133171
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.21227/en8q-2j47
title: Kinaesthetic Data Measured by Chai3d

description: Kinaesthetic data collected by Chai3D
DOI: 10.34740/kaggle/dsv/2082672
title: Grapevine Leaves
DOI: 10.34740/kaggle/dsv/2133354
title: Vietnam SARS-CoV-2 | COVID-19 Data
DOI: 10.3886/e138021v1
title: The Life Expectancy of Older Couples and Surviving Spouses

description: Individual life expectancies provide information for individuals making retirement decisions and for policy makers. For couples, analogous measures are the expected years both spouses will be alive (joint life expectancy) and the expected years the surviving spouse will be a widow or widower (survivor life expectancy). Using individual life expectancies to calculate summary measures for couples is intuitively appealing but yield misleading results, overstating joint life expectancy and dramatically understating survivor life expectancies. This implies that standard "individual life cycle models" are misleading for couples and that “couple life cycle models” must be substantially more complex. Using the CDC life tables for 2010, we construct joint and survivor life expectancy measures for randomly formed couples. The couples we form are defined by age, race and ethnicity, and education. Due to assortative marriage, inequalities in individual life expectancies are compounded into inequalities in joint and survivor life expectancies. We also calculate life expectancy measures for randomly formed couples for the 1930-2010 decennial years. Trends over time show how the relative rate of decrease in the mortality rates of men and women affect joint and survivor life expectancies. Because our couple life expectancy measures are based on randomly formed couples, they do not capture the effects of differences in spouses’ premarital characteristics (apart from sex, age, race and ethnicity, and, in some cases, education) or of correlations in spouses’ experiences or behaviors during marriage. However, they provide benchmarks which have been sorely lacking in the public discourse.
description: IPUMS variable perwt is used in the analysis.
description: Women aged 60 and their husbands of any age. Smallest Geographic Unit: United States
description: Mortality data was downloaded from:<br>CDC, National Center for Health Statistics. Life Tables. https://www.cdc.gov/nchs/products/life_tables.htm. August 3, 2017. <br><br>Census data was downloaded from:<br>Ruggles S, Genadek K, Goeken R, Grover J, Sobek M. Integrated Public Use Microdata Series: Version 7.0 [dataset]. Minneapolis: University of Minnesota, 2017. https://doi.org/10.18128/D010.V7.0.
DOI: 10.5281/zenodo.4301737
title: Saraga: research datasets of Indian Art Music

description: <strong>Dataset introduction</strong> This repository contains time aligned melody, rhythm, and structural annotations for two large open corpora of Indian Art Music (Carnatic and Hindustani music). The repository contains Carnatic and Hindustani collections in separated zip files, and each collection is organized by songs grouped by artist concerts/live performances. This organization follows the structure generated by downloading the data using the scripts available at the dataset Github repository: https://github.com/MTG/saraga. Moreover, there is a part of the Carnatic collection, 168 tracks to be specific, that counts with multitrack audio files apart from the mix audio. The considered instruments are: Ghatam, Mridangam, Violin, Voice and Secondary Voice. <strong>Annotations in the dataset</strong> Section and tempo annotations stored as start and end timestamps together with the name of the section and tempo during the section (in a separate file). Sama annotations referring to rhythmic cycle boundaries stored as timestamps. Phrase annotations stored as timestamps and transcription of the phrases using solfège symbols ({S, r, R, g, G, m, M, P, d, D, n, N}). Audio features automatically extracted and stored: pitch and tonic. For more information about the dataset tracks and annotations, please refer to the Saraga website: https://mtg.github.io/saraga/ <strong>Using this dataset</strong> We are interested in knowing if you find our datasets useful! If you use our dataset please email us at mtg-info@upf.edu and tell us about your research. *Please note that you can also use this dataset through the MIRDATA library (https://github.com/mir-dataset-loaders/mirdata), where this dataset is in the list of available datasets.
DOI: 10.5281/zenodo.4068196
title: Mridangam Stroke Dataset

description: The Mridangam Stroke dataset is a collection of 6977 audio examples of individual strokes of the Mridangam in various tonics. The dataset comprises of 10 different strokes played on Mridangams with 6 different tonic values. This is an updated version of the dataset, as the original version 1.0 presents some silent or wrong annotated tracks. <strong>Audio content</strong> The dataset provides audio examples for each of the strokes. There are six different tonics and ten different stroke labels. The audio examples were recorded from a professional Carnatic percussionist in a semi-anechoic studio conditions by Akshay Anantapadmanabhan using SM-58 microphones and an H4n ZOOM recorder. The audio was sampled at 44.1 kHz and stored as 16 bit wav files. The dataset can be used for training models for each Mridangam stroke. <strong>Metadata</strong> The whole dataset is organized by the tonic, into 6 packs. Each audio file is named as, <pre><code>&lt;StrokeName&gt;_&lt;Tonic&gt;_&lt;InstanceNumber&gt;.wav &lt;Tonic&gt; = {B, C, Csh, D, Dsh, E} &lt;StrokeName&gt; = {Bheem, Cha, Dheem, Dhin, Num, Ta, Tha, Tham, Thi, Thom}</code></pre> <strong>Using this dataset</strong> A detailed description of the Mridangam and its strokes can be found in the following paper. A part of the dataset was used in the paper. Please cite it if you use the dataset in your work. Akshay Anantapadmanabhan, Ashwin Bellur, Hema A. Murthy, "Modal analysis and transcription of strokes of the mridangam using non-negative matrix factorization," in Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013), pp.181-185, May 2013 http://hdl.handle.net/10230/25756 We are interested in knowing if you find our datasets useful! If you use our dataset please email us at mtg-info@upf.edu and tell us about your research. <strong>Contact</strong> If you have any questions or comments about the dataset, please feel free to write to us: Akshay Anantapadmanabhan (akshay.anantapadmanabhan@gmail.com) http://compmusic.upf.edu/mridangam-stroke-dataset
description: {"references": ["Akshay Anantapadmanabhan, Ashwin Bellur, Hema A. Murthy, \"Modal analysis and transcription of strokes of the mridangam using non-negative matrix factorization,\" in Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013), pp.181-185, May 2013"]}
DOI: 10.5281/zenodo.4699544
title: PowNet-Laos v1.3: Unit Commitment / Economic Dispatch model for the Laotian power system (with sample data)

description: This repository contains PowNet-Laos, an implementation of PowNet model on the Laotian power system. The model is developed based on the power system facilities (power plants, substations, import/export nodes, and high-voltage transmission lines) operated in Laos during the year 2016. The majority of data are extracted from technical reports, publicly available, published by Electricite Du Laos (EDL) and the Department of Energy Policy and Planning (DEPP), Ministry of Energy and Mine, Lao PDR. The input data concerning the production of the hydropower reservoirs are generated via simulation with VIC-Res. We note that these data could be generated with other hydrological-hydraulic models. Please refer to Chowdhury et al. (2020a) for additional details on (1) the Laotian power system, and (2) PowNet-Laos setup. The basic functionalities and PowNet input-output structure are described in Chowdhury et al. (2020b). Step-by-step instructions on how to run and customize PowNet for any power system are provided in this GitHub repository. References: Chowdhury, A.K., Dang, T.D., Bagchi, A., and Galelli, S., (2020a). Expected benefits of Laos' hydropower development curbed by hydro-climatic variability and limited transmission capacity—opportunities to reform. Journal of Water Resources Planning and Management, DOI: 10.1061/(ASCE)WR.1943-5452.0001279, (preprint). Chowdhury, A.K., Kern, J., Dang, T.D. and Galelli, S., (2020b). PowNet: A Network-Constrained Unit Commitment/Economic Dispatch Model for Large-Scale Power Systems Analysis. Journal of Open Research Software, 8(1), p.5. DOI: http://doi.org/10.5334/jors.302
description: {"references": ["Chowdhury, A.K., Dang, T.D., Bagchi, A., and Galelli, S., (2020a). Expected benefits of Laos' hydropower development curbed by hydro-climatic variability and limited transmission capacity\u2014opportunities to reform. Journal of Water Resources Planning and Management (in review).", "Chowdhury, A.K., Kern, J., Dang, T.D. and Galelli, S., (2020b). PowNet: A Network-Constrained Unit Commitment/Economic Dispatch Model for Large-Scale Power Systems Analysis. Journal of Open Research Software, 8(1), p.5. DOI: http://doi.org/10.5334/jors.302"]}
DOI: 10.25345/c5qb9w
title: MassIVE MSV000087228 - AF-positive and negative model as a demo dataset
title: AF-positive and negative model as a demo dataset for exploring the chemical molecules
DOI: 10.5281/zenodo.4699563
title: PowNet-Thailand v1.3: Network-constrained Unit Commitment / Economic Dispatch model for the Thai power system with sample data

description: PowNet-Thailand is an implementation of PowNet model on the Thai power system. The model is developed based on the load (domestic and exports) and infrastructure (power plants, substations, and high-voltage transmission lines) of the Thai power system operated in 2016, including a number of Laotian hydro and thermal power plants that supply to Thailand. The power systems’ data are mostly collected from publicly available reports, published by Electricity Generating Authority of Thailand (EGAT), Energy Policy and Planning Office (EPPO) – Thai Ministry of Energy, Electricite Du Laos (EDL), and the Department of Energy Policy and Planning (DEPP) – Laotian Ministry of Energy and Mine. The hydropower budget are simulated with VIC-Res. Please refer to Chowdhury et al. (2020a) for additional details on the power system and model setup. For 2016, the repository contains input data, output data, and a Python script for a sample analysis. In addition, climate-dependent input data (e.g., hydropower budget) for a 30-year (1976-2005) simulation period, used in Chowdhury et al. (2020a), are also provided. Outputs for any of these years can be obtained by running PowNet-Thailand through the following two steps: (1) change simulation year in ‘pownet_thai_datasetup.py’, and (2) run ‘pownet_thai_solver.py’. The basic functionalities and input-output structure of PowNet are described in Chowdhury et al. (2020b). Step-by-step instructions on how to run and customize PowNet for any power system are provided in this GitHub repository. If you use PowNet-Thailand and/or the data for your research, please cite the following papers: Chowdhury, A.K., Dang, T.D., Nguyen, H.T., Koh, R., and Galelli, S., (2020a). The Greater Mekong's climate-water-energy nexus: how ENSO-triggered regional droughts affect power supply and CO<sub>2</sub> emissions. Submitted to Earth’s Future, DOI: https://doi.org/10.1002/essoar.10504393.1. Chowdhury, A.K., Kern, J., Dang, T.D. and Galelli, S., (2020b). PowNet: A Network-Constrained Unit Commitment/Economic Dispatch Model for Large-Scale Power Systems Analysis. Journal of Open Research Software, 8(1), p.5. DOI: http://doi.org/10.5334/jors.302.