cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/2hy3-9268
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. The tweets were captured by an on-going project deployed at https://live.rlamsal.com.np. The geolocation data was extracted from the tweets which mentioned anything about “corona”, "coronavirus", "covid" and possible variants of "sarscov2", "nCov", "covid-19", "ncov2019". 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. Please note that this dataset should be solely used for non-commercial research purposes (ignore every other LICENSE category given on this page).Note: I started sharing the IDs of the tweets that had exact 'point' location information, only since April 28, 2020, with some genuine requests coming in from academic researchers who did not want to hydrate the whole lists of IDs (above 160+ million tweets) shared in the Coronavirus (COVID-19) Tweets Dataset.If you need the geolocation-based data starting March 20, 2020, then use the Coronavirus (COVID-19) Tweets Dataset and hydrate the IDs while adding the following condition:data = json.loads(data)if data["coordinates"]: longitude, latitude = data["coordinates"]["coordinates"]The data is available in two formats: CSV and JSON. I'll be sharing new files every day, and the files will be named period-wise. For example, april28-june5.zip will contain tweet ID and sentiment score of the tweets (in CSV and JSON formats) that were created between April 28, 2020, and June 05, 2020.Why are only tweet IDs being shared? It is Twitter's content redistribution policy that restricts the sharing of tweet information other than tweet IDs and/or user IDs. Twitter wants researchers to always pull fresh data using their API. Why? Here's my opinion. Maybe, some user might want to delete a particular tweet after a couple of minute(s)/hour(s)/day(s), and if the same tweet has already been pulled and is shared on a public domain, then it might make the user/community vulnerable to many inferences coming out of the shared data.
DOI: 10.14457/kku.the.1995.98
title: การศึกษาความคิดเห็นของบุคลากรทางการศึกษา ที่มีต่อการดำเนินงานของคณะกรรมการการประถมศึกษาจังหวัดนครราชสีมา
DOI: 10.14457/kku.the.1995.99
title: พฤติกรรมจริยธรรมและแนวโน้มพฤติกรรมจริยธรรม ในการอนุรักษ์ทรัพยากรธรรมชาติและสิ่งแวดล้อม ตามคำรายงานของนักศึกษามหาวิทยาลัยขอนแก่น
DOI: 10.14457/kku.the.1995.100
title: การศึกษาบุคลิกภาพด้านการปรับตัวทางสังคม ตามแนวทฤษฎีอิริคสันของข้าราชการตำรวจ ในระดับสถานีตำรวจภูธร ในสังกัด ตำรวจภูธรจังหวัดขอนแก่น
DOI: 10.14457/kku.the.1995.101
title: การสร้างโปรแกรมบทเรียนคอมพิวเตอร์ช่วยสอน เรื่องประจุไฟฟ้า ระดับชั้นมัธยมศึกษาปีที่ 5
DOI: 10.17034/63c300e0-fee1-4fe4-b2ea-e9ea44190fa5
title: A simulation of the agglomeration of 55 MNPs suspended in water at 300K

description: The initial agglomeration of 55 10nm radius magnetic nanoparticles in a periodic box, suspended in a fluid with the viscosity of water at 300K. As the simulation progresses the particles start to coalesce however the simulation is too short to see the formation of longer chains of MNPs. The spherical mesh represents the size of the particles (10nm radius), the central black sphere is the centre of the MNPs. The periodic box is 1000nm along each edge. The simulation time of the video is 3.3 × 10^4 ps. Contact person is author. E: jcook05@qub.ac.uk
DOI: 10.17034/903575ce-fca6-4300-9d47-c86743347f84
title: Mango: A molecular dynamics simulation engine

description: The code implemented during the related doctoral project has been made available here. Mango is a molecular dynamics simulator of magnetic nanoparticles written primarily in Python. It is designed to be versatile, enabling the classical simulation of the dynamics of any magnetic particle in a solution using given parameters about the particles and the solution. The code is licenced and available under the BSD 3-Clause licence. Contact person for dataset: James Cook E: jcook05@qub.ac.uk
DOI: 10.5281/zenodo.3886585
title: FALL DATABASE

description: The database consists of falls and activities of daily living performed by two persons (person1 and person2) – each person performed all activities twice. Hence, the database consists of 72 video sequences, containing 40 falls and 32 activities of daily living. The different scenarios are adopted from Noury et al. [1] and are described in [2]. Use The dataset is freely available for non-commercial research use. Please also cite our paper [2] when using the dataset for your research. References [1] N. Noury, A. Fleury, P. Rumeau, a K. Bourke, G. O. Laighin, V. Rialle, and J. E. Lundy, “Fall detection – principles and methods.,” in Engineering in Medicine and Biology Society, 2007, vol. 2007, pp. 1663–1666. [2] Planinc R., Kampel M., “Robust Fall Detection by Combining 3D Data and Fuzzy Logic”, ACCV Workshop on Color Depth Fusion in Computer Vision, Daejeon, Korea, pp. 121-132, November 2012.
DOI: 10.5281/zenodo.3886595
title: Indoor Skeleton Tracking Dataset

description: Description This database contains skeleton tracking information obtained by the Asus Xtion pro live sensor in combination with OpenNI. It contains 3 different indoor environments: 90 days of tracking data from a living room, 74 days of tracking data within a kitchen as well as 20 days of tracking data within an office environment. The dataset has been used for evaluation in [1]. Use The dataset is freely available for non-commercial research use. Please also cite our paper [1] when using the dataset for your research. Technical Details The dataset contains 3 folders, one for each scenario. The tracking information is provided within csv files (e.g. 1.csv), where one day of tracking information is saved per CSV file. Corresponding ground plane estimation results are provided in textfiles (e.g. 1_groundPlane.txt), containing the estimated ground plane parameters a, b, c and d for the 1st day. The csv files include the following information: <em>FrameNr, Timestamp, FPS (can be ignored)</em> Moreover, tracking information (x, y, z, confidence) provided by OpenNI of the following skeleton joints is available: <em>Center of mass, left and right hip, spine, shoulder center, head, left and right knee, left and right foot, left and right shoulder, left and right elbow, left and right hand</em> References [1] Planinc R., Kampel M. “Human Centered Scene Understanding based on 3D Long-Term Tracking Data”, <em>Proceedings of the Iberian Conference on Pattern Recognition and Image Analysis (IbPria)</em>, to appear, Santiago de Compostela, Spain, June 2015.
DOI: 10.5281/zenodo.3886596
title: Indoor Skeleton Tracking Dataset

description: Description This database contains skeleton tracking information obtained by the Asus Xtion pro live sensor in combination with OpenNI. It contains 3 different indoor environments: 90 days of tracking data from a living room, 74 days of tracking data within a kitchen as well as 20 days of tracking data within an office environment. The dataset has been used for evaluation in [1]. Use The dataset is freely available for non-commercial research use. Please also cite our paper [1] when using the dataset for your research. Technical Details The dataset contains 3 folders, one for each scenario. The tracking information is provided within csv files (e.g. 1.csv), where one day of tracking information is saved per CSV file. Corresponding ground plane estimation results are provided in textfiles (e.g. 1_groundPlane.txt), containing the estimated ground plane parameters a, b, c and d for the 1st day. The csv files include the following information: <em>FrameNr, Timestamp, FPS (can be ignored)</em> Moreover, tracking information (x, y, z, confidence) provided by OpenNI of the following skeleton joints is available: <em>Center of mass, left and right hip, spine, shoulder center, head, left and right knee, left and right foot, left and right shoulder, left and right elbow, left and right hand</em> References [1] Planinc R., Kampel M. “Human Centered Scene Understanding based on 3D Long-Term Tracking Data”, <em>Proceedings of the Iberian Conference on Pattern Recognition and Image Analysis (IbPria)</em>, to appear, Santiago de Compostela, Spain, June 2015.
DOI: 10.6073/pasta/b888aea39223562457700e6a15bad42f
title: Whole-Tree Nonstructural Carbohydrate Budgets in Five Species at Harvard Forest 2014

description: We measured nonstructural carbohydrate (NSC) concentrations throughout the year in the branches, stemwood, and roots of five temperate tree species. These NSC concentrations were used in two ways. First, we scaled up concentrations to the whole-tree level using allometric equations and compared NSC storage between these five species to determine the size and seasonal fluctuation of whole-tree total NSC budgets as well as the contribution of individual organs. Second, for four of these species, we assessed the radial patterns and seasonality of NSC concentrations in the stemwood based on contrasting wood anatomy (ring-porous vs. diffuse-porous).
DOI: 10.5066/p9pgxs53
title: Nanoscale Molecular Composition of Solid Bitumen from the Eagle Ford Group Across a Natural Thermal Maturity Gradient

description: Solid bitumen is a petrographically-defined secondary organic matter residue produced during petroleum generation and subsequent oil transformation. The presence of solid bitumen impacts many shale reservoir properties including porosity, permeability, and hydrocarbon generation and storage, amongst others. Furthermore, solid bitumen reflectance is an important parameter for assessing the thermal maturity of formations with little to no vitrinite. While the molecular composition of solid bitumen will strongly impact associated parameters such as the development of organic matter porosity, hydrocarbon generation, and optical reflectance, assessing the molecular composition of solid bitumen in situ within shale reservoirs can be challenged by small grain sizes (often 1 m in diameter) and the inherent heterogeneity of shale formations. Here we employ the recently developed atomic force microscopy-based infrared spectroscopy (AFM-IR) to investigate solid bitumen molecular composition in situ within shale samples from the Late Cretaceous Eagle Ford Group possessing Type II-S kerogen that span a natural thermal maturity gradient from early oil-generation to the dry gas window. The application of AFM-IR allows for the rapid collection of thousands of measurements with ~50 nm resolution from the interrogated solid bitumen grains. Our results indicate that: (i) solid bitumen from the lower Eagle Ford displays both intra- and intergranular molecular variation, (ii) this molecular variation tends to, but not universally, decrease with an increase in thermal maturity, and (iii) the solid bitumen composition between samples, from an atomic ratio perspective, is more similar than analysis of kerogen isolates would indicate. These findings are discussed with perspective toward understanding the impact of thermal stress on the composition of secondary organic matter within the Eagle Ford Shale and highlight the growing awareness that organic matter heterogeneity within petroliferous mudrocks extends down to the nanoscale regime.
DOI: 10.17632/6sz8h6hnpz.1
title: ExaHyPE: An engine for parallel dynamically adaptive simulations of wave problems

description: ExaHyPE (“An Exascale Hyperbolic PDE Engine”) is a software engine for solving systems of first-order hyperbolic partial differential equations (PDEs). Hyperbolic PDEs are typically derived from the conservation laws of physics and are useful in a wide range of application areas. Applications powered by ExaHyPE can be run on a student’s laptop, but are also able to exploit thousands of processor cores on state-of-the-art supercomputers. The engine is able to dynamically increase the accuracy of the simulation using adaptive mesh refinement where required. Due to the robustness and shock capturing abilities of ExaHyPE’s numerical methods, users of the engine can simulate linear and non-linear hyperbolic PDEs with very high accuracy. Users can tailor the engine to their particular PDE by specifying evolved quantities, fluxes, and source terms. A complete simulation code for a new hyperbolic PDE can often be realised within a few hours — a task that, traditionally, can take weeks, months, often years for researchers starting from scratch. In this paper, we showcase ExaHyPE’s workflow and capabilities through real-world scenarios from our two main application areas: seismology and astrophysics.
DOI: 10.5281/zenodo.3886597
title: CitieS-Health Barcelona Survey Results

description: This dataset contains the data collected using an online survey on knowledge, perceptions and preferences on topics to be investigated around the theme of air pollution and health in Barcelona, Spain. The data collected are for the CitieS-Health project in Barcelona. A scientific paper based on the online survey results is under review.
DOI: 10.5281/zenodo.3886598
title: CitieS-Health Barcelona Survey Results

description: This dataset contains the data collected using an online survey on knowledge, perceptions and preferences on topics to be investigated around the theme of air pollution and health in Barcelona, Spain. The data collected are for the CitieS-Health project in Barcelona. A scientific paper based on the online survey results is under review.
DOI: 10.5281/zenodo.3886386
title: Valvometry experiments: King scallops (Pecten maximus L.) gape distance inferred from Hall voltage, using Hall-Effect Sensors.

description: An experimental study performed on king scallops (Pecten maximus, L.) provided a comprehensive set of valvometry data. Gape distance was inferred from variations of the Hall-voltage, measured by an HES connected to a strain recorder. The protocole, data calcibration and data series for 12 different individuals are provided in the dataset.'
DOI: 10.5281/zenodo.3886385
title: Valvometry experiments: King scallops (Pecten maximus L.) gape distance inferred from Hall voltage, using Hall-Effect Sensors.

description: An experimental study performed on king scallops (Pecten maximus, L.) provided a comprehensive set of valvometry data. Gape distance was inferred from variations of the Hall-voltage, measured by an HES connected to a strain recorder. The protocole, data calcibration and data series for 12 different individuals are provided in the dataset.'
DOI: 10.5281/zenodo.3886414
title: PBMC 3k test datasets for besca

description: This is a single cell transcriptomics dataset containing roughly 3,000 PBMCs. The original data was downloaded from the Seurat 3k PBMC tutorial: https://satijalab.org/seurat/v3.0/pbmc3k_tutorial.html. We reprocessed the dataset using the Besca package (https://github.com/bedapub/besca).
description: {"references": ["Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36(5):411\u2010420. doi:10.1038/nbt.4096", "https://satijalab.org/seurat/v3.0/pbmc3k_tutorial.html"]}
DOI: 10.5519/qd.bkz4ia51
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.21227/k5x5-e989
title: 300-Dimensional Word Embeddings for Nepali Language

description: This pre-trained Word2Vec model has 300-dimensional vectors for more than 0.5 million Nepali words and phrases. A separate Nepali language text corpus was created using the news contents freely available in the public domain. The text corpus contained more than 90 million running words.Word2Vec model details: Embeddings Dimension: 300, Architecture: Continuous - BOW, Training algorithm: Negative sampling = 15, Context (window) size: 10, Token minimum count: 2, Encoded in UTF-8.
DOI: 10.6073/pasta/b77012b1775901df15d9adc92d5e06fc
title: Data for study Conventional land-use intensification reduces species richness and increases production: A global meta-analysis

description: Most current research on land‐use intensification addresses its potential to either threaten biodiversity or to boost agricultural production. However, little is known about the simultaneous effects of intensification on biodiversity and yield. To determine the responses of species richness and yield to conventional intensification, this dataset was created and a global meta‐analysis on it was carried out, thus synthesizing 115 studies. The dataset consists of 449 cases that cover a variety of areas used for agricultural (crops, fodder) and silvicultural (wood) production. It was found that across all production systems and species groups, conventional intensification is successful in increasing yield (grand mean + 20.3%), but it also results in a loss of species richness (−8.9%). However, analysis of sub‐groups revealed inconsistent results. Within high‐intensity systems species losses were non‐significant but yield gains were substantial (+15.2%). Conventional intensification within medium intensity systems revealed the highest yield increase (+84.9%) and showed the largest loss in species richness (−22.9%). Production systems differed in their magnitude of richness response, with insignificant changes in silvicultural systems and substantial losses in crop systems (−21.2%). In addition, this meta‐analysis identifies a lack of studies that collect robust biodiversity (i.e. beyond species richness) and yield data at the same sites and that provide quantitative information on land‐use intensity. These findings suggest that, in many cases, conventional land‐use intensification drives a trade‐off between species richness and production. However, species richness losses were often not significantly different from zero, suggesting even conventional intensification can result in yield increases without coming at the expense of biodiversity loss. These results, which were published in a paper titled Conventional land‐use intensification reduces species richness and increases production: A global meta‐analysis, could guide future research to close existing research gaps, and to understand the circumstances required to achieve such win‐win or win‐no‐harm situations in conventional agriculture.
DOI: 10.6073/pasta/e2e44d8cc9228b169b2f80ff0dd1e470
title: Experiment on Competition and the Local Distribution of the Grass Stipa neomexicana, Arizona, 1979 - 1986

description: This dataset contains the results on an experiment whose aim was to determine whether competitive displacement accounted for a species' local distribution. Within a grassland in southern Arizona, Stipa neomexicana, a C3 grass, was found to occur only on dry ridge crests with low total grass cover, while total grass cover is greater below the ridge crests in moister, low—lying areas. It was hypothesized that Stipa neomexicana was limited to these dry ridges by competitive exclusion. This hypothesis was tested by removal experiments conducted at three positions along the topographic gradient. The responses of Stipa were compared with those of Aristida glauca and other neighboring grass species, all of which are C4 grasses (Stipa being the only C3 grass in that area). This experiment was carried out from 1980 to 1983, in a grassland near Sonoita, Santa Cruz County, Arizona, USA.
DOI: 10.5281/zenodo.3822036
title: Data and code for "A Library of Late Transition Metal Alloy Dielectric Functions for Nanophotonic Applications"

description: This data set contains dielectric functions for the ten binary alloys comprised of the late transition metals most commonly employed in plasmonics (Ag, Au, Cu, Pd, Pt).
DOI: 10.5281/zenodo.3822037
title: Data and code for "A Library of Late Transition Metal Alloy Dielectric Functions for Nanophotonic Applications"

description: This data set contains dielectric functions for the ten binary alloys comprised of the late transition metals most commonly employed in plasmonics (Ag, Au, Cu, Pd, Pt).
DOI: 10.5281/zenodo.3886437
title: Evolution of COVID-19 by country until 28th May

description: This dataset is a three dimensional dataset in wich we analyze the evolution of some data related with COVID-19 along the time. We analyse how a type of data behave along the time in the different countries. In each csv, we have kind of varibale (Cases, recovered, deaths) by country and date (from 03/30 to 05/28). So we have 5 time series by country: one for each kind of data. The csv contais the information related with a kind of data, and are described by the other two dimensions: country and date. We obtained this dataset scrapping Worldometers.
description: This dataset its updated until 2020/05/04. However, the dataset is up to date in https://github.com/AdrianArnaiz/scrap_uoc, where its updated automatically every day.
description: {"references": ["Worldometers. Covid-19 coronavirus pandemic. https://www.worldometers.info/coronavirus/, 2020.", "Ensheng Dong, Hongru Du, and Lauren Gardner. An interactive web-based dashboard to track covid-19 in real time. The Lancet infectious diseases, 2020.", "Richard Lawson. Web scraping with Python. Packt Publishing Ltd, 2015", "Max Roser, Hannah Ritchie, and Esteban Ortiz-Ospina. Coronavirus disease (covid-19)\u2013statistics and research. Our World in Data, 2020.", "Laia Subirats Mate and Mireia Calvo Gonzalez. Web scraping. Technical report, UOC, Barcelona, (sf). PID00256970."]}
DOI: 10.34725/dvn/akrouq/qojpjr
title: Uganda_insects_data.xlsx

description: Uganda insects data