cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/sbyfzp22tr.2
title: Revisão de trabalhos correlacionados

description: Este arquivo refere-se a lista de trabalhos finais selecionados como trabalhos correlacionados a pesquisa realizada sobre Interação apoiada pela utilização de um Sistema de Relatório com DashBoard para suporte ao docente de curso EAD
DOI: 10.17632/9bkjv99bdk.2
title: Data from: Mapping Knowledge Gaps of Mozambique’s Terrestrial Mammals

description: This dataset has been used for the analysis presented in the paper "Mapping knowledge gaps of Mozambique’s Terrestrial Mammals", by the same authors, published at Scientific Reports. A resourceful strategy to support conservation planning is to assess knowledge gaps from primary species-occurrence data to identify and select areas for future biodiversity surveys. Currently, the increasing accessibility to these data allows a cost-effective manner to boost the knowledge on a country’s biodiversity. This is especially beneficial for understudied countries where the lack of resources for conservation is more pronounced. This is the case of the Republic of Mozambique which is a species-rich country yet poorly known. In the paper, we assessed and map the country’s knowledge gaps on terrestrial mammal species by identifying areas that are geographically distant and environmentally different from the well-known sites, based on the measurement of terrestrial mammal inventory's completeness in Mozambique, per grid cell at 0.25 degree resolution. Here, we present the list of species targeted by that study, a matrix with the species presence per grid cell, as well as results of completeness per grid cell obtained from the analysis of the complete inventory. For more details on the analysis performed, see Queirós Neves et al. (2019) Mapping Knowledge Gaps of Mozambique’s Terrestrial Mammals Sientific Reports 9 18184. https://doi.org/10.1038/s41598-019-54590-4.
DOI: 10.17632/gw5w7c285b.2
title: Do you need to practice structured sport and eat a lot of proteins for fat mass loss and maintain fat free mass?

description: Do you need to practice structured sport and eat a lot of proteins for fat mass loss and maintain fat free mass?
DOI: 10.21227/1nqs-nz47
title: Proprioceptive Sensor Dataset for Quadruped Robots

description: These datasets are of the hydraulically actuated robot HyQ’s proprioceptive sensors. They include absolute and relative encoders, force and torque sensors, and MEMS-based and fibre optic-based inertial measurement units (IMUs). Additionally, a motion capture system recorded the ground truth data with millimetre accuracy. In the datasets HyQ was manually controlled to trot in place or move around the laboratory. The sequence includes: forward and backwards motion, side-to-side motion, zig-zags, yaw motion, and a mix of linear and yaw motion. All of the datasets are at least five minutes long. The aim of these datasets is to test state estimation using only proprioceptive sensors.
DOI: 10.17632/w9wkd7g43f.3
title: Authorship Attribution Dataset in Bangla 2

description: A dataset consisting of text samples from 6 authors with 350 samples per author and total word count of 2.3+ million.
DOI: 10.17632/zt3k6jd9z7.1
title: Academic Cheating, Justification, Values 2016

description: College students were surveyed about how much they had engaged in various types of academic cheating, as well as whether they considered the behaviors to be cheating. They also answered questions about academic major and personal values.
DOI: 10.17632/m4ssv5mxrz.2
title: Dysprosium phospholyl paper

description: Research support data for Dysprosium phospholyl paper
DOI: 10.15129/62d7586a-8efe-4616-9e3f-f0033f8f6515
title: Data for: "Containing a Credible Loss to Within Frequency Stability Limits in a Low Inertia GB Power System"

description: Data contains one Excel file with on the cases presented in the paper entitled: "Containing a Credible Loss to Within Frequency Stability Limits in a Low Inertia GB Power System".
DOI: 10.5519/qd.uqv4g9ys
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (4131 records)
DOI: 10.5880/crc1211db.32
title: cpDNA alignments and R-skript for Historical Biogeography of Zygophyllaceae in the Atacama Desert

description: The dataset contains sequence information of the historical biogeographic study of the plant family Zygophyllaceae in the Atacama Desert. We provide three nexus files containing chloroplast sequence data used for all phylogenetic divergence time estimation analysis of the paper. The first nexus file (Zygophyllales_CP_Master.nex) contains the concatenated sequence alignment with character set annotations for the three-chloroplast marker (rbcL, trnL-trnF, trnS-trnG) as well as for hotspot regions and sequence inversions which were excluded and reverse complemented for analysis. The second nexus file (Zygophyllales_CP_Master_HSEX_INVREV_BEAST.nex) is identical to the first one except from the fact that all hotspot regions were excluded and the inversions reverse complemented. This dataset was used for the dating analyses. The third nexus file (Zygophyllales_CP_Master_HSEX_INVREV_RAxML.nex) was used for the phylogenetic reconstruction using a Maximum Likelihood approach. Finally, we provide the R-skript used to plot the time calibrated trees and the ancestral area reconstruction.
DOI: 10.5281/zenodo.3572672
title: Gene Ontology Data Archive

description: Archival bundle of GO data release.
description: {"references": ["Ashburner et al. Gene ontology: tool for the unification of biology (2000) Nat Genet 25(1):25-9. Online at Nature Genetics.", "GO Consortium, Nucleic Acids Res., 2017"]}
DOI: 10.21227/7he1-fy62
title: Brain Signals and the Corresponding Hand Movement Signals Dataset (BS-HMS-Dataset)

description: BS-HMS-Dataset is a dataset of the users' brainwave signals and the corresponding hand movement signals from a large number of volunteer participants. The dataset has two parts; (1) Neurosky based Dataset (collected over several months in 2016 from 32 volunteer participants), and (2) Emotiv based Dataset (collected from 27 volunteer participants over several months in 2019). Neurosky based Dataset - Neurosky based dataset consists of EEG data recordings from neurosky headset and the corresponding hand movements recordings from SONY smartwatch's motions sensors. The users wore the smartwatch based on their personal preference of left or right hand.Emotiv based Dataset - Emotiv based dataset consists of EEG data recordings from Emotiv Epoc+ headset and the corresponding hand movements from both the hands of the user. The hand movement data was recorded using two SONY smartwatches which users wore in their hands, one in each hand. Both the datasets, Neurosky based Dataset and Emotiv based Dataset, were collected in two different sessions with an average gap of 6-7 days between the sessions. In each session the volunteer participants performed four different activities; (1) watching a happy video, (2) typing their response to the questions about the story in the watched happy video, (3) watching a sad video, and (4) typing their response to the questions about the story in the watched sad video. The users took a gap of ~5 minutes to rest between the different activities. The data was recorded separately for each activity.
DOI: 10.5066/p9xmecvv
title: Organic Analysis of Oilfield Wastewater from the Williston Basin, North Dakota

description: The organic composition of produced waters (flowback and formation waters) from the Bakken Formation and the Three Forks Formation in the Williston Basin, North Dakota were examined in this study in order to aid in the remediation of surface contamination due to spills during transport and help develop treatment methods for recycling. Twelve produced water samples were collected from wells in the Bakken and Three Forks Formations at the well head and analyzed for non-purgable dissolved organic carbon (NPDOC), acetate, and extractable hydrocarbons. NPDOC and acetate concentrations from sampled wells from ranged from 33-190 milligrams per liter (mg/L) and 16-40 mg/L, respectively. Concentrations of individual extractable hydrocarbon compounds ranged from less than 1 to greater than 450 micrograms per liter (ug/L), and included polycyclic aromatic hydrocarbons (PAHs), phenolic compounds, glycol ethers, and cyclic ketones.
DOI: 10.15468/xhsajs
title: Description de Megapomponia foksnodi n. sp. (Rhynchota, Cicadidae, Pomponiini)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Boulard, Michel (2010): Description de Megapomponia foksnodi n. sp. (Rhynchota, Cicadidae, Pomponiini). Bulletin de la Société entomologique de France 115 (1): 15-16, DOI: http://doi.org/10.5281/zenodo.3572711
DOI: 10.7910/dvn/kfizjz/85wv8r
title: JBPA_sector_bias_USstudents_dataverse.tab

description: :unav
DOI: 10.7910/dvn/kfizjz/lpes1i
title: Meier and An (2019). Sector Bias in Public Programs codebook.pdf

description: :unav
DOI: 10.7910/dvn/kfizjz/q8wjmn
title: JBPA_JBPA_sector_bias_replication_dataverse.do

description: :unav
DOI: 10.5066/p95masav
title: Trends in selected streamflow metrics at reference streamgages in the conterminous United States, 1980-2014

description: This dataset includes four tables related to annual trends in streamflow metrics at 599 reference streamgages in the conterminous United States for the period 1955-2014. Reference streamgages are defined here as gages with drainage basins that are minimally impacted by anthropogenic effects such as reservoirs or urbanization. The four tables are: 1) computed annual values for 16 streamflow metrics, 2) trend estimates for the 16 streamflow metrics for the period 1955-2014, 3) metric names and definitions, and 4) location information (latitude and longitude) for the 599 sites. The computed annual values for the 16 streamflow metrics are: low flow magnitude, low flow frequency, low flow duration, high flow magnitude, high flow frequency, high flow duration, skew, daily rises, Spring low flow percentage, Spring high flow percentage, Summer low flow percentage, Summer high flow percentage, Fall low flow percentage, Fall high flow percentage, Winter low flow percentage, and Winter high flow percentage. The annual flow metrics were estimated by Eng and others (2017) from daily streamflow records at the 599 reference streamgages. Sen slope trend values and confidence intervals for the 16 flow metrics for the period 1955-2014 were computed from the annual time series using the non-parametric Theil-Sen approach (Sen, P., 1968). In addition to the Sen slope, the median value for each metric for the period 1955-2014, the percentage change in each metric over the period 1955-2014, and a percentage change class for each metric also are reported.
DOI: 10.21227/w796-2z48
title: UNS Hoverflies Classification Dataset

description: Dataset was created as part of joint efforts of two research groups from the University of Novi Sad, which were aimed towards development of vision based systems for automatic identification of insect species (in particular hoverflies) based on characteristic venation patterns in the images of the insects' wings.The set of wing images consists of high-resolution microscopic wing images of several hoverfly species. There is a total of 868 wing images of eleven selected hoverfly species from two different genera, Chrysotoxum and Melanostoma. Besides images of the whole wings "UNS_Hoverflies" dataset also consists of small image patches (64x64 pixels) corresponding to 18 predetermined landmark points in each wing, which were systematically collected from the preprocessed wing images and organized inside the second root folder named "Training - test set".Each wing specimen was uniquely numbered and associated with the corresponding taxonomy group.
DOI: 10.21227/yw7r-k939
title: A Machine Learning Benchmark for Facies Classification

description: The recent interest in using deep learning for seismic interpretation tasks, such as facies classification, has been facing a significant obstacle, namely the absence of large publicly available annotated datasets for training and testing models. As a result, researchers have often resorted to annotating their own training and testing data. However, different researchers may annotate different classes, or use different train and test splits. In addition, it is common for papers that apply deep learning for facies classification to not contain quantitative results, and rather rely solely on visual inspection of the results. All of these practices have lead to subjective results and have greatly hindered the ability to compare different machine learning models against each other and understand the advantages and disadvantages of each approach.To address these issues, we open-source an accurate 3D geological model of the Netherlands F3 Block. This geological model is based on both well log data and 3D seismic data and is grounded on the careful study of the geology of the region. Furthermore, we propose two baseline models for facies classification based on deconvolution networks and make their codes publicly available. Finally, we propose a scheme for evaluating different models on this dataset, and we share the results of our baseline models. In addition to making the dataset and the code publicly available, this work can help advance research in this area and create an objective benchmark for comparing the results of different machine learning approaches for facies classification for researchers to use in the future.
DOI: 10.21227/5ahd-pg92
title: Cocoa beans spectral image with three fermentation levels

description: The target scene consists of a black cardwith six cocoa beans of three different fermentation levels (High, correct, and low fermentation),two beans for each class, whosefalse-color composite is shown in the provided Figure (a),ground-truth map is shownin Fig. (b), and Fig. (c) presents its representative spectralsignatures. The spectral imagewas acquired by the AVT Stingray F-080Bcamera by acquiring one band each time from350 - 950 nm. The acquired image hasa spatial resolution of 1096x712 pixels and 300 spectral bands of 2 nm width.
DOI: 10.21227/k1x9-5v32
title: Plantar Thermogram Database for the Study of Diabetic Foot Complications

description: The presented database contains thermal images (thermograms) of the plantar region. The database was obtained from 122 subjects with a diabetes diagnosis (DM group) and 45 non-diabetic subjects (control group). The relevance of this database consists in to study how the temperature is distributed in the plantar region of both groups and how their differences can be measured. Previous reports in the literature have established that an increase in the plantar temperature is associated with a higher ulceration risk. Therefore, it is important to study how the thermal changes in the plantar region occur and how they can be measured in order to develop new diagnostic support tools that help the medical expert to take preventive actions. The thermogram database was obtained from 122 diabetic and 45 non-diabetic subjects. Extra information about the analysis of the thermograms for diabetic foot complications can be founded in the scientific reports of the corresponding author link.
DOI: 10.21227/y8nk-yb30
title: High Resolution Photoluminescence Microscopy Images And Spectra Of Air-Suspended Single-Wall Carbon Nanotubes

description: The dataset contains high-resolution microscopy images and confocal spectra of semiconducting single-wall carbon nanotubes. Carbon nanotubes allow down-scaling of electronic components to the nano-scale. There is initial evidence from Monte Carlo simulations that microscopy images with high digital resolution show energy information in the Bessel wave pattern that is visible in these images. In this dataset, images from Silicon and InGaAs cameras, as well as spectra, give valuable insights into the spectroscopic properties of these single-photon emitters. The source data is connected to a GitLab pipeline running on docker containers for data processing to various formats that are valid input to machine learning algorithms to further prove that there is information about the excitonic emission wavelength contained in the microscopic images.
DOI: 10.5519/qd.3mks4073
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (331945 records)
DOI: 10.21227/s5ty-fe70
title: Proprioceptive Sensor Dataset for Quadruped Robots

description: These datasets are of the hydraulically actuated robot HyQ’s proprioceptive sensors. They include absolute and relative encoders, force and torque sensors, and MEMS-based and fibre optic-based inertial measurement units (IMUs). Additionally, a motion capture system recorded the ground truth data with millimetre accuracy. In the datasets HyQ was manually controlled to trot in place or move around the laboratory. The sequence includes: forward and backwards motion, side-to-side motion, zig-zags, yaw motion, and a mix of linear and yaw motion. All of the datasets are at least five minutes long. The aim of these datasets is to test state estimation using only proprioceptive sensors.
DOI: 10.21227/tjyb-xq19
title: Heidelberg Spiking Datasets

description: The Heidelberg Spiking Datasets comprise two spike-based classification datasets: The Spiking Heidelberg Digits (SHD) dataset and the Spiking Speech Command (SSC) dataset. The latter is derived from Pete Warden's Speech Commands dataset (https://arxiv.org/abs/1804.03209), whereas the former is based on a spoken digit dataset recorded in-house and included in this repository. Both datasets were generated by applying a detailed inner ear model to audio recordings. We distribute the input spikes and target labels in HDF5 format. SHD as well as SSC are released under the Creative Commons Attribution 4.0 International License.
DOI: 10.21227/v0ky-0p46
title: WiFi RSSI Indoor Localization

description: A reliable and comprehensive public WiFi fingerprinting database for researchers to implement and compare the indoor localization’s methods.The database contains RSSI information from 6 APs conducted in different days with the support of autonomous robot.We use an autonomous robot to collect the WiFi fingerprint data. Our 3-wheel robot has multiple sensors including wheel odometer, an inertial measurement unit (IMU), a LIDAR, sonar sensors and a color and depth (RGB-D) camera. The robot can navigate to a target location to collect WiFi fingerprints automatically. The localization accuracy of the robot is 0.07 m ± 0.02 m. The dimension of the area is 21 m × 16 m. It has three long corridors. There are six APs and five of them provide two distinct MAC address for 2.4- and 5-GHz communications channels, respectively, except for one that only operates on 2.4-GHz frequency. There is one router can provide CSI information.
DOI: 10.21227/w3qk-2b91
title: Python 3.6 (Spyder) program file_Detection of corrupt data in a PV plant database

description: This folder contains two csv files and one .py file. One csv file contains NIST canopy PV plant data imported from https://pvdata.nist.gov/. This csv file has 1041 days raw data consisting PV plant POA irradiance, ambient temperature, Inverter DC current, DC voltage, AC current and AC voltage. Second csv file contains user created data. The Python file imports two csv files. The Python program executes four proposed corrupt data detection methods to detect corrupt data in NIST canopy PV plant data. The program generates many PDF and JPG image plots to represent the findings of four methods. A PV plant user can use this Python program by replacing the NIST canopy PV plant data with their plant data to detect corrupt days.
DOI: 10.21227/yhfv-2r24
title: Twitter Sentiment Analysis Data

description: Each database (*.db) contain three columns. First column: date and time of the tweet, second column: tweet, third column: sentiment score for the particular tweet within the range [-1,1] with -1 being the most negative, 0 being the neutral and +1 being the most positive sentiment.The tweets have been collected by the LSTM model deployed here at sentiment.live[1]. The last column, viz. sentiment score, is not the score estimated by the model. The LSTM model is still in the beta phase. Therefore, to make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of TextBlob [2] has been appended as the last column. The sentiment scores produced by our model will be made public after the project's documentation part is finished.####status/latest addition: Nov 24, 2019####Tweets containing the term "android": 0.88 million-plusTweets containing the term "oneplus": 15 thousand-plusTweets containing the term "messi": 225 thousand-plusTweets containing the term "housefull 4": 346 thousand-plusTweets containing the term "the joker": 1 million-plusTweets containing the term "iphone 11": 2 million-plus##################A new database will be added here every week. Please bookmark this page for the updates.References:[1]https://sentiment.live/ [2]https://textblob.readthedocs.io/en/dev/
DOI: 10.7910/dvn/rskawd/zgari1
title: airpotentials.h

description: :unav
DOI: 10.21227/vcsb-hm54
title: PRECIS HAR

description: PRECIS HAR represents a RGB-D dataset for human activity recognition, captured with the 3D camera Orbbec Astra Pro. It consists of 16 different activities (stand up, sit down, sit still, read, write, cheer up, walk, throw paper, drink from a bottle, drink from a mug, move hands in front of the body, move hands close to the body, raise one hand up, raise one leg up, fall from bed, and faint), performed by 50 subjects. There are 800 videos in total, having both RGB and depth recordings.The dataset does not contain other people than the video subject that appear as environmental noise. Besides, the background does not change. However, its difficulties are:Small camera occlusions: from time to time, we added small occlusions to the RGB stream (we move the mouse from a part of the screen to the other), that simulate the presence of external, uncontrolled environment factors; for example, in a real scenario, a fly could pass in front of the camera.No stage directions: while filming the dataset, we did not instruct participants on how to perform each action. We simply told them the name of the action. Thus, they all interpreted them slightly differently, closer to the natural way in which a person would behave.Variable video length: because we did not impose constraints on how the participants had to perform each action, dataset videos have different lengths.Different lighting conditions: the source of light is variable, sometimes having scenes with pale light and other times with sun shining directly on scene parts. These lighting variations are however only visible in the RGB stream. The depth stream is not affected by environmental changes, as far as the light is concerned. We thus want to emphasize the utility of also examining the depth stream, while classifying videos from RGB-D cameras. When citing this dataset, please cite both the dataset and the article that introduces it: Fusion Mechanisms for Human Activity Recognition using Automated Machine Learning. Instructions will be added here once the article is published.
DOI: 10.21227/eg2f-4j44
title: Proprioceptive Sensor Dataset for Quadruped Robots

description: These datasets are of the hydraulically actuated robot HyQ’s proprioceptive sensors. They include absolute and relative encoders, force and torque sensors, and MEMS-based and fibre optic-based inertial measurement units (IMUs). Additionally, a motion capture system recorded the ground truth data with millimetre accuracy. In the datasets HyQ was manually controlled to trot in place or move around the laboratory. The sequence includes: forward and backwards motion, side-to-side motion, zig-zags, yaw motion, and a mix of linear and yaw motion. All of the datasets are at least five minutes long. The aim of these datasets is to test state estimation using only proprioceptive sensors.