cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4395322
title: Sponge dwelling-fauna from the Northwestern Tropical Atlantic Ocean: a bibliographic records database.

description: English New version of the long-awaited database of guest/host relationships in reef sponges. +500 new records of 19 different papers were added. With this update we have a more complete database and information from groups such as Cnidarians, Fish and Mollusks is recovered. Do not miss it! If you would like to collaborate with this project contact me: antarmijail@comunidad.unam.mx Gulf of Mexico and Caribbean sea sponge dwelling-fauna data base. Data extracted of index papers. Aviable at http://globalbioticinteractions.org` Español Nueva versión de la tan esperada base de datos de relaciones huésped/hospedero en esponjas arrecifales. Se agregaron +500 nuevos registros de 19 artículos diferentes. Con esta actualización se tiene una base de datos más completa y se recupera información de grupos como Cnidarios, Peces y Moluscos aparte de sumar nueva información sobre artrópodos No te la pierdas Si te gustaría colaborar con este proyecto contácteme: antarmijail@comunidad.unam.mx Base de datos de fauna huésped asociada a esponjas arrecifales del golfo de México y mar Caribe. Registros extraídos de publicaciones en revistas indexadas. Información disponible en http://globalbioticinteractions.org`
DOI: 10.6084/m9.figshare.13280252.v2
title: Global offshore wind farm dataset

description: This dataset provide geocoded information on global offshore wind farms (OWFs) derived from Sentinel-1 synthetic aperture radar (SAR) time-series images from 2015 to 2019. It identified 7,112 wind turbines comprising of more than 10 nations. Data is available at 10 m spatial resolution, providing an explicit dataset for planning, monitoring and managing marine space. The global OWFs are stored in Shapefile (.shp) format. The attributes and metadata are organized with referenced to the WGS84 datum, and each record consists of eight attributes: identifier (owf_id), centroid latitude (centr_lat), centroid longitude (centr_lon), continent, country, sea area (sea_area), appearance year (occ_year) and month (occ_month).<br>
DOI: 10.18112/openneuro.ds003430.v1.0.1
title: Visual image reconstruction from human brain activity during selective attention unveils subjective visual images
DOI: 10.21227/mt3a-vd26
title: Fluorescence Microscopy Images from CBMI

description: Intracellular organelle networks such as the endoplasmic reticulum (ER) network and the mitochondrial network serve crucial physiological functions. Morphology of these networks plays critical roles in mediating their functions.Accurate image segmentation is required for analyzing morphology of these networks for applications such as disease diagnosis and drug discovery. Deep learning models have shown remarkable advantages in accurate and robust segmentation of these complex network structures. To support the training and testing of deep learning segmentation models, we construct two fluorescence image datasets, ER and MITO, for the ER network and the mitochondrial network, respectively. We provide manual segmentation for these organelle networks in binary masks. The datasets have been used to evaluate and compare performance of the methods proposed in our article “Heuristic Optimization of Deep Learning Models for Segmentation of Intracellular Organelle Networks”.
DOI: 10.18112/openneuro.ds003430.v1.0.2
title: Visual image reconstruction from human brain activity during selective attention unveils subjective visual images
DOI: 10.21227/3zb7-0w98
title: Fluorescence Microscopy Images from CBMI

description: Intracellular organelle networks such as the endoplasmic reticulum (ER) network and the mitochondrial network serve crucial physiological functions. Morphology of these networks plays critical roles in mediating their functions.Accurate image segmentation is required for analyzing morphology of these networks for applications such as disease diagnosis and drug discovery. Deep learning models have shown remarkable advantages in accurate and robust segmentation of these complex network structures. To support the training and testing of deep learning segmentation models, we construct two fluorescence image datasets, ER and MITO, for the ER network and the mitochondrial network, respectively. We provide manual segmentation for these organelle networks in binary masks. The datasets have been used to evaluate and compare performance of the methods proposed in our article “Heuristic Optimization of Deep Learning Models for Segmentation of Intracellular Organelle Networks”.
DOI: 10.6084/m9.figshare.13116578
title: Nanocomposite NiTi shape memory alloy with high strength and fatigue resistance

description: The source data is uploaded to support our publication in Nature Nanotechnology (DOI: https://doi.org/10.1038/s41565-020-00837-5). Citation is required for using the data.<br>AbstractFatigue life enhancement of NiTi-based shape memory alloys (SMA) has been an active topic over the past decades, because many established and potential applications of NiTi SMA in biomedical devices and recent solid-state refrigeration require very long fatigue life (10<sup>7</sup>-10<sup>9</sup>). However, improving the fatigue resistance of NiTi often compromises other mechanical and functional properties. Here we provide a new strategy to overcome this dilemma through hybrid heterogenous nanostructure. We produced superelastic NiTi nanocomposite with crystalline and amorphous phases via severe plastic deformation and low temperature annealing. We show that the NiTi nanocomposite has a recoverable strain of 4.3%, yield strength of 2.3 GPa, and can endure over 10<sup>8</sup> reversible-phase-transition cycles under the stress of 1.8 GPa via compression experiments of micropillars. We attribute the enhanced properties to the mutual strengthening of nano-sized amorphous and crystalline phases where both dislocation slip in the crystals and shear band propagation in the amorphous phase are suppressed. The synergy of the properties of crystalline and amorphous phases at the nanoscale could be an effective method to improve fatigue resistance and strength of SMA.
DOI: 10.6084/m9.figshare.13116578.v1
title: Nanocomposite NiTi shape memory alloy with high strength and fatigue resistance

description: Fatigue life enhancement of NiTi-based shape memory alloys (SMA) has been an active topic over the past decades, because many established and potential applications of NiTi SMA in biomedical devices and recent solid-state refrigeration require very long fatigue life (10<sup>7</sup>-10<sup>9</sup>). However, improving the fatigue resistance of NiTi often compromises other mechanical and functional properties. Here we provide a new strategy to overcome this dilemma through hybrid heterogenous nanostructure. We produced superelastic NiTi nanocomposite with crystalline and amorphous phases via severe plastic deformation and low temperature annealing. We show that the NiTi nanocomposite has a recoverable strain of 4.3%, yield strength of 2.3 GPa, and can endure over 10<sup>8</sup> reversible-phase-transition cycles under the stress of 1.8 GPa via compression experiments of micropillars. We attribute the enhanced properties to the mutual strengthening of nano-sized amorphous and crystalline phases where both dislocation slip in the crystals and shear band propagation in the amorphous phase are suppressed. The synergy of the properties of crystalline and amorphous phases at the nanoscale could be an effective method to improve fatigue resistance and strength of SMA.
DOI: 10.6084/m9.figshare.13490617
title: R Markdown file from Aminopeptidase-N modulation assists lean mass anabolism during refuelling in the white-throated sparrow

description: File contains all code used to produce figures and accompanying statistical analyses for the manuscript.
DOI: 10.6084/m9.figshare.13490617.v1
title: R Markdown file from Aminopeptidase-N modulation assists lean mass anabolism during refuelling in the white-throated sparrow

description: File contains all code used to produce figures and accompanying statistical analyses for the manuscript.
DOI: 10.6084/m9.figshare.13490610
title: lncRNAs

description: 1285 autophagy-related lncRNAs
DOI: 10.6084/m9.figshare.13490610.v1
title: lncRNAs

description: 1285 autophagy-related lncRNAs
DOI: 10.6084/m9.figshare.13487049
title: Brainstorm_reconstruction_files

description: In starting a Brainstorm protocol these files will have to be placed into the folder structure it creates.
DOI: 10.6084/m9.figshare.13487028
title: Fieldtrip format .set files for Matlab

description: These .set files can were used to produce the resultant stats files in the Matlab folder, with the scripts that are also included there
DOI: 10.6084/m9.figshare.13487004
title: behavioural_data

description: Raw behavioural .dat files, and .xlsx file of data sorting, normalized based on total bias per participant, median calculations and figure construction, trial category calculations for labeling of EEG epoch data. SPSS output also included for General linear model and paired samples t-test
DOI: 10.6084/m9.figshare.13487004.v1
title: behavioural_data

description: Raw behavioural .dat files, and .xlsx file of data sorting, normalized based on total bias per participant, median calculations and figure construction, trial category calculations for labeling of EEG epoch data. SPSS output also included for General linear model and paired samples t-test
DOI: 10.6084/m9.figshare.13487196
title: topographical plots for 25 ms windows of effect

description: topographical plots for 25 ms windows of effect for difference between conditions 1 and 3.
DOI: 10.6084/m9.figshare.13487196.v1
title: topographical plots for 25 ms windows of effect

description: topographical plots for 25 ms windows of effect for difference between conditions 1 and 3.
DOI: 10.6084/m9.figshare.13487049.v1
title: Brainstorm_reconstruction_files

description: In starting a Brainstorm protocol these files will have to be placed into the folder structure it creates.
DOI: 10.6084/m9.figshare.13487028.v1
title: Fieldtrip format .set files for Matlab

description: These .set files can were used to produce the resultant stats files in the Matlab folder, with the scripts that are also included there
DOI: 10.34740/kaggle/dsv/1787154
title: New York City Taxi Trips
DOI: 10.21227/w16q-cj05
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains IDs and sentiment scores of the geo-tagged tweets related to the COVID-19 pandemic. The tweets are captured by an on-going project deployed at https://live.rlamsal.com.np. The model monitors the real-time Twitter feed for coronavirus-related tweets using 90+ different keywords and hashtags that are commonly used while referencing the pandemic. Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location.The paper associated with this dataset is available here: Design and analysis of a large-scale COVID-19 tweets dataset-------------------------------------Related datasets: (a) Coronavirus (COVID-19) Tweets Sentiment Trend (Global)(b) Tweets Originating from India During COVID-19 Lockdowns-------------------------------------Below is the quick overview of this dataset.— Dataset name: GeoCOV19Tweets Dataset— Number of tweets : 288,084 tweets— Coverage : Global— Language : English (EN)— Dataset usage terms : By using this dataset, you agree to (i) use the content of this dataset and the data generated from the content of this dataset for non-commercial research only, (ii) remain in compliance with Twitter's Developer Policy and (iii) cite the following paper:Lamsal, R. Design and analysis of a large-scale COVID-19 tweets dataset. Applied Intelligence (2020). https://doi.org/10.1007/s10489-020-02029-z— Primary dataset : Coronavirus (COVID-19) Tweets Dataset (COV19Tweets Dataset)— Dataset updates : Everyday— Active keywords and hashtags: keywords.tsvPlease visit this page (primary dataset) for details regarding the collection date and time (and other notes) of each CSV file present in this dataset.
DOI: 10.5517/ccdc.csd.cc245jkf
title: CCDC 1971524: Experimental Crystal Structure Determination

description: Related Article: Zhishuo Wang, Linfeng Jiang, Jinwen Ji, Fulin Zhou, Jingbo Lan, Jingsong You|2020|Angew.Chem.,Int.Ed.|59|23532|doi:10.1002/anie.202010051
DOI: 10.5517/ccdc.csd.cc249f85
title: CCDC 1975266: Experimental Crystal Structure Determination

description: Related Article: Zhishuo Wang, Linfeng Jiang, Jinwen Ji, Fulin Zhou, Jingbo Lan, Jingsong You|2020|Angew.Chem.,Int.Ed.|59|23532|doi:10.1002/anie.202010051
DOI: 10.5517/ccdc.csd.cc24dd87
title: CCDC 1978118: Experimental Crystal Structure Determination

description: Related Article: Han-Wen Yang, Ping Xu, Bo Ding, Xiu-Guang Wang, Zheng-Yu Liu, Hong-Kun Zhao, Xiao-Jun Zhao, En-Cui Yang|2020|Cryst.Growth Des.|20|7615|doi:10.1021/acs.cgd.0c00544
DOI: 10.5517/ccdc.csd.cc24dd98
title: CCDC 1978119: Experimental Crystal Structure Determination

description: Related Article: Han-Wen Yang, Ping Xu, Bo Ding, Xiu-Guang Wang, Zheng-Yu Liu, Hong-Kun Zhao, Xiao-Jun Zhao, En-Cui Yang|2020|Cryst.Growth Des.|20|7615|doi:10.1021/acs.cgd.0c00544
DOI: 10.5517/ccdc.csd.cc24ddb9
title: CCDC 1978120: Experimental Crystal Structure Determination

description: Related Article: Han-Wen Yang, Ping Xu, Bo Ding, Xiu-Guang Wang, Zheng-Yu Liu, Hong-Kun Zhao, Xiao-Jun Zhao, En-Cui Yang|2020|Cryst.Growth Des.|20|7615|doi:10.1021/acs.cgd.0c00544
DOI: 10.5517/ccdc.csd.cc24vbgt
title: CCDC 1991516: Experimental Crystal Structure Determination

description: Related Article: Timo Schillmöller, Paul Niklas Ruth, Regine Herbst-Irmer, Dietmar Stalke|2020|Chem.-Eur.J.|26|17390|doi:10.1002/chem.202003017
DOI: 10.5517/ccdc.csd.cc24vbhv
title: CCDC 1991517: Experimental Crystal Structure Determination

description: Related Article: Timo Schillmöller, Paul Niklas Ruth, Regine Herbst-Irmer, Dietmar Stalke|2020|Chem.-Eur.J.|26|17390|doi:10.1002/chem.202003017
DOI: 10.5517/ccdc.csd.cc24vbjw
title: CCDC 1991518: Experimental Crystal Structure Determination

description: Related Article: Timo Schillmöller, Paul Niklas Ruth, Regine Herbst-Irmer, Dietmar Stalke|2020|Chem.-Eur.J.|26|17390|doi:10.1002/chem.202003017
DOI: 10.5517/ccdc.csd.cc24vbkx
title: CCDC 1991519: Experimental Crystal Structure Determination

description: Related Article: Timo Schillmöller, Paul Niklas Ruth, Regine Herbst-Irmer, Dietmar Stalke|2020|Chem.-Eur.J.|26|17390|doi:10.1002/chem.202003017
DOI: 10.5517/ccdc.csd.cc24vbly
title: CCDC 1991520: Experimental Crystal Structure Determination

description: Related Article: Timo Schillmöller, Paul Niklas Ruth, Regine Herbst-Irmer, Dietmar Stalke|2020|Chem.-Eur.J.|26|17390|doi:10.1002/chem.202003017
DOI: 10.5517/ccdc.csd.cc24vbmz
title: CCDC 1991521: Experimental Crystal Structure Determination

description: Related Article: Timo Schillmöller, Paul Niklas Ruth, Regine Herbst-Irmer, Dietmar Stalke|2020|Chem.-Eur.J.|26|17390|doi:10.1002/chem.202003017
DOI: 10.5517/ccdc.csd.cc24vbn0
title: CCDC 1991522: Experimental Crystal Structure Determination

description: Related Article: Timo Schillmöller, Paul Niklas Ruth, Regine Herbst-Irmer, Dietmar Stalke|2020|Chem.-Eur.J.|26|17390|doi:10.1002/chem.202003017
DOI: 10.5517/ccdc.csd.cc24vbp1
title: CCDC 1991523: Experimental Crystal Structure Determination

description: Related Article: Timo Schillmöller, Paul Niklas Ruth, Regine Herbst-Irmer, Dietmar Stalke|2020|Chem.-Eur.J.|26|17390|doi:10.1002/chem.202003017
DOI: 10.5517/ccdc.csd.cc24vbq2
title: CCDC 1991524: Experimental Crystal Structure Determination

description: Related Article: Timo Schillmöller, Paul Niklas Ruth, Regine Herbst-Irmer, Dietmar Stalke|2020|Chem.-Eur.J.|26|17390|doi:10.1002/chem.202003017
DOI: 10.5517/ccdc.csd.cc24vbs4
title: CCDC 1991526: Experimental Crystal Structure Determination

description: Related Article: Timo Schillmöller, Paul Niklas Ruth, Regine Herbst-Irmer, Dietmar Stalke|2020|Chem.-Eur.J.|26|17390|doi:10.1002/chem.202003017
DOI: 10.5517/ccdc.csd.cc24vbt5
title: CCDC 1991527: Experimental Crystal Structure Determination

description: Related Article: Timo Schillmöller, Paul Niklas Ruth, Regine Herbst-Irmer, Dietmar Stalke|2020|Chem.-Eur.J.|26|17390|doi:10.1002/chem.202003017
DOI: 10.5517/ccdc.csd.cc24vbr3
title: CCDC 1991525: Experimental Crystal Structure Determination

description: Related Article: Timo Schillmöller, Paul Niklas Ruth, Regine Herbst-Irmer, Dietmar Stalke|2020|Chem.-Eur.J.|26|17390|doi:10.1002/chem.202003017
DOI: 10.5517/ccdc.csd.cc235xcl
title: CCDC 1942099: Experimental Crystal Structure Determination

description: Related Article: Ani Ozcelik, Daniel Aranda, Sara Gil-Guerrero, Xaquín A. Pola-Otero, Maria Talavera, Liangxuan Wang, Santosh Kumar Behera, Johannes Gierschner, Ángeles Peña-Gallego, Fabrizio Santoro, Raquel Pereira-Cameselle, J. Lorenzo Alonso-Gómez|2020|Chem.-Eur.J.|26|17342|doi:10.1002/chem.202002561
DOI: 10.34740/kaggle/dsv/1788681
title: New York City Taxi Trips
DOI: 10.5281/zenodo.4395467
title: IMGDroid's dataset -21 apps

description: This is one of the data sets used by IMGDroid, which includes 21 Android apps collected from F-Droid. Please note that these apps in the data set can only be used for research. The copyright is preserved by the app vendor.
DOI: 10.5281/zenodo.4392284
title: IMGDroid's dataset -21 apps

description: This is one of the data sets used by IMGDroid, which includes 21 Android apps collected from F-Droid. Please note that these apps in the data set can only be used for research. The copyright is preserved by the app vendor.
DOI: 10.5281/zenodo.4392727
title: AIOps Systematic Mapping Study - Results

description: Results from our work "A Systematic Mapping Study in AIOps" (https://arxiv.org/abs/2012.09108). The file 'papers.csv' contains the complete list of AIOps papers identified, with corresponding metadata (author, year, citations, venue) and indexing annotations (macro-area, category, data sources, etc.) as columns. This file enables to explore and reproduce our results from scratch. For a quick and interactive exploration of the results without coding, you can check out the same dataset on Exploratory.io (https://exploratory.io/project/EDK0DNx1Qe/AIOps___Mapping_Study_pXe9pUn2).
description: {"references": ["https://arxiv.org/abs/2012.09108"]}
DOI: 10.5281/zenodo.4392726
title: AIOps Systematic Mapping Study - Results

description: Results from our work "A Systematic Mapping Study in AIOps" (https://arxiv.org/abs/2012.09108). The file 'papers.csv' contains the complete list of AIOps papers identified, with corresponding metadata (author, year, citations, venue) and indexing annotations (macro-area, category, data sources, etc.) as columns. This file enables to explore and reproduce our results from scratch. For a quick and interactive exploration of the results without coding, you can check out the same dataset on Exploratory.io (https://exploratory.io/project/EDK0DNx1Qe/AIOps___Mapping_Study_pXe9pUn2).
description: {"references": ["https://arxiv.org/abs/2012.09108"]}
DOI: 10.21227/8gf4-df85
title: Biomarker of Lateralization Index for Stress Calculated from Hemodynamic Responses of fNIRS for Subdividing between Eustress and Distress

description: While performing various mental stress tasks, we can easily observe differences in brain activations between the stress group and the control group (i.e., non-stress group). Moreover,it is essential to subdivide the stress groups into eustress and distress groups in order to enhance personal cognitive abilities and mental/physical health. However, it is very challenging to subdivide stress group, because of the difficulty to reveal differences in brain activations between two stress groups. In our study, we aim to ascertain the necessity of subdividing two stress groups screened by salivary alpha-amylase (sAA). Furthermore, we develop a potential biomarker to subdivide two stress groups using hemodynamic responses of functional near-infrared spectroscopy (fNIRS). We stimulate two stress groups divided by sAA with an international affective picture system (IAPS) inducing positive or negative emotions and measure hemodynamic responses at the same time. As a result, we have developed a newly designed biomarker, lateralization index for stress (LIS) calculated from hemodynamic responses, resulting in separating the two stress groups. The two stress groups show distinct LIS values in the visually dominant positive session. Therefore, using fNIRS, we, for the first time to the best of our knowledge, figure out that the differences in brain activations among three groups (control vs. eustress vs. distress groups) are distinguishable.
DOI: 10.21227/ngx8-vn73
title: Biomarker of Lateralization Index for Stress Calculated from Hemodynamic Responses of fNIRS for Subdividing between Eustress and Distress

description: While performing various mental stress tasks, we can easily observe differences in brain activations between the stress group and the control group (i.e., non-stress group). Moreover,it is essential to subdivide the stress groups into eustress and distress groups in order to enhance personal cognitive abilities and mental/physical health. However, it is very challenging to subdivide stress group, because of the difficulty to reveal differences in brain activations between two stress groups. In our study, we aim to ascertain the necessity of subdividing two stress groups screened by salivary alpha-amylase (sAA). Furthermore, we develop a potential biomarker to subdivide two stress groups using hemodynamic responses of functional near-infrared spectroscopy (fNIRS). We stimulate two stress groups divided by sAA with an international affective picture system (IAPS) inducing positive or negative emotions and measure hemodynamic responses at the same time. As a result, we have developed a newly designed biomarker, lateralization index for stress (LIS) calculated from hemodynamic responses, resulting in separating the two stress groups. The two stress groups show distinct LIS values in the visually dominant positive session. Therefore, using fNIRS, we, for the first time to the best of our knowledge, figure out that the differences in brain activations among three groups (control vs. eustress vs. distress groups) are distinguishable.
DOI: 10.14457/tu.the.2019.914
title: ผลกระทบที่ cryptocurrency มีต่อการสร้าง efficient portfolio: กรณี SET100

description: การศึกษานี้มีวัตถุประสงค์เพื่อศึกษาผลกระทบของ Cryptocurrency ต่อผลตอบแทนของ portfolio โดยการทดสอบผลตอบแทนของ portfolio ที่มีและไม่มี Cryptocurrency ใน 3 สกุล อันประกอบด้วย Bitcoin, Ethereum, และ Ripple การศึกษานี้วิเคราะห์ข้อมูลโดยการเปรียบเทียบ Sharpe Ratio จากการจำลอง portfolio ดังกล่าว ระหว่างช่วง สิงหาคม ปี พ.ศ.2558 ถึง สิงหาคม ปี พ.ศ.2562 ผลการศึกษาพบว่า Cryptocurrency ทำให้มี Efficiency สูงขึ้นและมี Standard Deviation ที่ต่ำกว่า
DOI: 10.14457/tu.the.2019.915
title: ผลลัพธ์ของการใช้บอร์ดเกมต่อการลงทุนและการวางแผนการเงิน

description: การศึกษานี้มีจุดประสงค์เพื่อศึกษาผลลัพธ์ของการใช้บอร์ดเกมต่อการลงทุนและการวางแผนการเงิน โดยการเก็บรวบรวมและวิเคราะห์ข้อมูลจากการสัมภาษณ์ผู้ให้บริการบอร์ดเกม และผู้ที่มีประสบการณ์ในการเล่นบอร์ดเกมรวมจำนวน 10 คนในระหว่างเดือนพฤษภาคมถึงเดือนกรกฎาคม พ.ศ.2563 ผลการศึกษาพบว่าบอร์ดเกมสามารถช่วยให้เข้าใจในหลักการของการลงทุนและการวางแผนการเงินได้มากยิ่งขึ้น ทำให้ตระหนักถึงความสำคัญของการวางแผนการเงินต่อความเสี่ยงในชีวิต