cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.26999512.v1
title: High Quality Honey Bee Dataset

description: Honey bee datasetThe dataset contains 157,529 high quality images of centred and cropped honey bees from video data.Specification85 individuals (10 drones, 75 worker bees)Individuals only, centred and cropped to 512x512 pixelsGood colour fidelity, uniform lightingFiles grouped by contiguous path segments with an average length of 46 frames and a standard deviation of 51 frames.Video captured at 60 frames per secondA total of 3430 trajectories and 157529 images are availableFolder structureThe first level separates the 87 individuals and gives information on sex and pollen-bearing trait:<b>[worker/drone]_</b><b>[pollen/nopollen]</b><b>_</b><b><i>YYYY-MM-DDTHH-MM-SS</i></b><b>_</b>The second level enumerates the trajectories of each individual:<b>[01234]</b>The data is contained in the third level as .jpg files. A .csv file contains the x and y coordinates of the cropped bee in the original video file.Capturing procedureEach individual was recorded for 5 minutes. Trajectories were broken when the individual was close to the edge of the image, making it impossible to capture the centre of the image.<br>
DOI: 10.6084/m9.figshare.7451015.v2
title: Source Data

description: These are the raw datasets that generated Fig. 3 - Fig. 7, Supplementary Fig. 1 - Fig. 4 in the paper entitled "Characterization of the hemodynamic response function in white matter tracts for event-related fMRI."
DOI: 10.6084/m9.figshare.8969126.v1
title: Additional file 12: of Discovery and characterization of the evolution, variation and functions of diversity-generating retroelements using thousands of genomes and metagenomes

description: Table S7. DGRs with huge variation and short CCS in TR-VR pairs. URL: http://cgm.sjtu.edu.cn/index/pub/software/MetaCSST/supplementary/Supplementary_Table_7.xlsx (XLSX 50 kb)
DOI: 10.17632/4kczwc59v4
title: The Perceived Risk of Freshwater Oil Pipelines and Spills in Icy Conditions

description: Using the psychometric paradigm of risk in conjunction with surveys of the Michigan public (n = 569) and a regional planning organization (n = 65), we examine the perceived risk associated with underwater oil pipelines, the Enbridge Line 5 pipeline in particular, and oil spills under ice. The fate of Line 5 is heavily debated in Michigan, specifically the portion that traverses the Straits of Mackinac, which can be ice-covered for months. Scant literature examines either our capabilities for detecting and remediating oil spills in ice-covered conditions or how individuals perceive the risk associated with Line 5 or its alternatives. Here we identify considerable concern regarding both the pipeline and the potential for spills under ice on behalf of the public, and increased concern about spills under ice on behalf of the planning organization. Organization members' concerns are significantly predicted by beliefs about the difficulty in remediating spills, however not by beliefs about spills' likelihood, detection, noticeability, or consequences. Our results identify the need to better understand the fate of oil captured under ice, develop practical guidelines for remediating such spills, and use decision-making processes that explicitly analyze the social, economic and environmental tradeoffs of underwater pipelines.
DOI: 10.17632/hgkv9cpnmn
title: Datasets and source code for a pipeline architecture for feature-based unsupervised clustering using multivariate time series from HPC jobs

description: This repository is composed of 2 compressed files, with the contents as next described.

--- code.tar.gz ---
The source code that implements the pipeline, as well as code and scripts needed to retrieve time series, 
create the plots or run the experiments. More specifically:

	+ prepare.py and main.py ⇨ 
		The Python programs that implement the pipeline, both the auxiliary and the main pipeline 
		stages, respectively. 

	+ 'anomaly' and 'config' folders ⇨ 
		Scripts and Python files containing the configuration and some basic functions that are 
		used to retrieve the information needed to process the data, like the actual resource 
		time series from OpenTSDB, or the job metadata from Slurm.
			
	+ 'functions' folder ⇨ 
		Several folders with the Python programs that implement all the stages of the pipeline, 
		either for the Machine Learning processing (e.g., extractors, aggregators, models), or 
		the technical aspect of the pipeline (e.g., pipelines, transformer).
				
	+ plotDF.py ⇨ 
		A Python program used to create the different plots presented, from the resource time 
		series to the evaluation plots.
	
	+ several bash scripts ⇨ 
		Used to run the experiments using a specific configuration, whether regarding which 
		transformers are chosen and how they are parametrized, or more technical aspects 
		involving how the pipeline is executed.

--- data.tar.gz ---
The actual data and results, organized as follows:

	+ jobs ⇨ 
		All the jobs' resource time series plots for all the experiments, with a folder used 
		for each experiment. Inside each folder all the jobs are separated according to their 
		id, containing the plots for the different system resources (e.g., User CPU, Cached memory).
				
	+ plots ⇨ 
		All the predictions' plots for all the experiments in separated folders, mainly used for 
		evaluation purposes (e.g., scatter plot, heatmaps, Andrews curves, dendrograms). These 
		plots are available for all the predictors resulting from the pipeline execution. In 
		addition, for each predictor it is also possible to visualize the resource time series 
		grouped by clusters. Finally, the projections as generated by the dimension reduction 
		models, and the outliers detected, are also available for each experiment.
	
	+ datasets ⇨ 
		The datasets used for the experiments, which include the lists of job IDs to be processed 
		(CSV files) and the results of each stage of the pipeline (e.g., features, predictions), 
		and the output text files as generated by several pipeline stages. Among these latter 
		files it is worth to note the evaluation ones, that include all the predictions scores.
DOI: 10.17632/tm4f8x73d4
title: Data for: Comparison of Sustainability Models in Development of Electric Vehicles in Tehran Using Fuzzy TOPSIS Method

description: Sustainable development based policy making utilizing fuzzy TOPSIS
DOI: 10.6084/m9.figshare.19771759.v1
title: Datasets - Research Integrity – Needs and provision of training in Australian Institutions

description: Accompanying datasets to a survey conducted through 2020-2021, looking at the perceived provision and quality of training in research integrity by institutions within Australia. Included: Raw data (anonymised) Report data - top-level pooling of data and crosstabulations with key demographic questions Career stage data - questions broken down by differing career stages Questionnaire - finalised questions used for data collection
DOI: 10.6084/m9.figshare.12916025.v1
title: Gene database of Pyrococcus abyssi GE5

description: <i>Pyrococcus abyssi </i>is a hyperthermophilic archaeon isolated from deep-sea hydrothermal vents. Anaerobic, motile, Gram-negative, and cocci in shape, the archaeon has an optimal growth temperature of 96 <sup>o</sup>C. This work sought to provide fundamental information about the genetic repertoire of <i>P. abyssi</i> GE5 by parsing its annotated genome sequence downloaded from Genbank. Comprising gene name, gene function, and gene sequence, the resource should be useful for fundamental microbiology studies seeking to understand the genetic toolsets that afford <i>P. abyssi</i> the ability to survive at high temperatures.
DOI: 10.17632/sfwkmvmmd5
title: EEG data (from real-time neurofeedback experiment)

description: EEG_epochs_sample.npy: numPy array of size [1200, 550, 32] corresponding to [trials, samples, channels]. The data is recorded with a sampling frequency of 500Hz using Enobio 32 (Neuroelectrics), i.e. 1100 ms of data (100 ms pre-stimulus onset, 1000 ms post-stimulus onset). The channels are ordered as: ['P7', 'P4', 'Cz', 'Pz', 'P3', 'P8', 'O1', 'O2', 'T8', 'F8', 'C4', 'F4', 'Fp2', 'Fz', 'C3', 'F3', 'Fp1', 'T7', 'F7', 'Oz', 'PO3', 'AF3', 'FC5', 'FC1', 'CP5', 'CP1', 'CP2', 'CP6', 'AF4', 'FC2', 'FC6', 'PO4']. y_categories_sample.npy: Contains binary category labels for each (visual) trial. 0 corresponds to a "scene" category. 1 corresponds to a "face" category. More information here: https://github.com/gretatuckute/ClosedLoop/blob/master/README.md
DOI: 10.6084/m9.figshare.14501964.v2
title: A gene coevolution network provides insight to eukaryotic cellular and genomic structure and function

description: data associated with Steenwyk et al. 2022, An orthologous gene coevolution network provides insight into eukaryotic cellular and genomic structure and function.
DOI: 10.6084/m9.figshare.23498982.v1
title: Research Data (Health & Environmental).xlsx

description: Title: Dataset for IoT-Based Remote Health Monitoring System for Asthma Patients <br> Description: The sensor data from an Internet of Things-based remote health monitoring system created for people with asthma is included in this collection. The information includes both environmental and health factors, giving details on the patients' health and indoor environments. The dataset is a useful tool for researching the efficiency of the monitoring system and examining asthma patients' medical problems. <br> Environmental Data: The file contains measurements of the room's temperature, humidity, and dust density. A DHT11 sensor was used to measure temperature and humidity, while an optical dust sensor was used to measure dust concentration. Over the course of a month, the data was gathered every 15 minutes. <br> Health Data: The dataset also includes health parameters including body temperature, oxygen saturation, and heart rate (measured in beats per minute, or BPM). A MAX30100 sensor was used to measure the heart rate and the amount of oxygen in the blood, while a DS18B20 sensor was used to gauge the body temperature. Over the course of one week, measurements for each of these parameters were conducted at regular intervals on one patient. <br> Data Analysis: A thorough data analysis, including descriptive analysis, graphical representation, and statistical testing, has been performed on the dataset. For both environmental and health factors, descriptive analysis involves computing a number of statistical measures, including mean, standard deviation, minimum, maximum, and percentage of data outside permissible limits. To see the trends and patterns in the data, graphs were created, including line plots, box plots, and time series plots. In order to investigate the variations and importance of health markers throughout various time periods, statistical tests like ANOVA were carried out. <br> Data Reproducibility: Researchers may use the technique described in the corresponding paper to replicate the data. This involves connecting the sensors (DHT11, MAX30100, DS18B20) for data collecting, setting up the IoT-based monitoring system using NodeMCU and Arduino microcontrollers, and employing the relevant software platforms (Blynk, Thingspeak) for real-time monitoring and data visualization. The corresponding author will provide more thorough instructions upon request.
DOI: 10.6084/m9.figshare.24033402.v2
title: Kragel 270 participant dataset (18 studies x 15 participants) from Kragel et al. 2018 Nature Neuroscience

description: <br><br>This dataset is an fmri_data object class object created using CANlab tools (canlab.github.io). It contains 270 single-participant fMRI contrast maps across 18 studies (with 15 participants per study). Studies are grouped into three domains: Pain, Cognitive Control, and Negative Emotion, with 9 studies each. Each domain includes 3 subdomains, with 3 studies each. <br>The dataset is from Kragel et al. 2018, Nature Neuroscience. Kragel, P. A., Kano, M., Van Oudenhove, L., Ly, H. G., Dupont, P., Rubio, A., Delon-Martin, C., Bonaz, B. L., Manuck, S. B., Gianaros, P. J., Ceko, M., Losin, R. E. A., Woo, C. W., Nichols, T. E., Wager, T. D. (2018). Generalizable representations of pain, cognitive control, and negative emotion in medial frontal cortex. <i>Nature neuroscience</i>. 21(2): 283-289. PMCID: PMC5801068.https://www.nature.com/articles/s41593-017-0051-7it's also stored on Neurovault.org as collection #504. Using CANlab tools , you could get it using:[files_on_disk, url_on_neurovault, mycollection, myimages] = retrieve_neurovault_collection(504);This file also has metadata that is not on Neurovault, in data_obj.metadata_table. If you save this file somewhere on your Matlab path, you'll be able to reload and reuse the dataset easily.<pre>[test_images, names] = load_image_set('kragel18_alldata', 'noverbose');<br></pre><br>270×6 tableDomain Subdomain imagenames Studynumber Orig_Studynumber StudyCodes_______________ ___________ __________________________ ___________ ________________ {'Pain' } {'Thermal'} {'ThermalPain1' } 1 10 {'Atlas_2010_EXP' }{'Pain' } {'Thermal'} {'ThermalPain1' } 1 10 {'Atlas_2010_EXP' }{'Pain' } {'Thermal'} {'ThermalPain1' } 1 10 {'Atlas_2010_EXP' }{'Pain' } {'Thermal'} {'ThermalPain1' } 1 10 {'Atlas_2010_EXP' }{'Pain' } {'Thermal'} {'ThermalPain1' } 1 10 {'Atlas_2010_EXP' }.... etcNote: Individual participant images are <i>not</i> on the same magnitude scale across studies. For most applications, you will want to normalize the scale (e.g., L2 norm or z-score images).e.g., in CANlab tools:data_obj = rescale(data_obj, 'l2norm_images');A .nii file format image is also included. These images are rescaled by dividing by the L2 norm of each image. <br>
DOI: 10.6084/m9.figshare.24533263.v1
title: Arabidopsis <i>PDS5</i> genes play pivotal roles in genome organization via suppressing TAD formation

description: Eukaryote genomes in interphase nuclei are organized in a non-random, hierarchical fashion that correlates to gene expression and other biological activities in the nucleus. One key structural element in the three-dimensional (3D) chromatin organization is the Topologically Associating Domain (TAD), which facilitates the creation of distinct chromatin compartments and specific chromatin interactions, including those between enhancers and their target genes. Similar chromatin compartments, known as TAD-like domains, have been identified in many plant species. However, they have been curiously sparse in the model plant Arabidopsis thaliana. In our recent research, we observed substantial formation of TAD-like domains in an Arabidopsis mutant plant. This webpage contains various types of datasets supporting our conclusion. Please check "Figshare_file_description.xlsx" for details of individual files.
DOI: 10.6084/m9.figshare.26495956.v1
title: NetGP

description: Some of the datasets and trained models used in NetGP.
DOI: 10.17632/js2nzwf5md
title: Dataset for "XRF and XRPD data sets in ternary mixtures with high level micro-absorption and/or preferred orientations problems for phase quantification analysis"

description: XRF and XRPD data were collected on four different mixture experimental domain, each containing one or more common issues for phase quantification: micro-absorption and preferred orientations. Simplex-Centroid experimental design was used to homogeneously explore the experimental domain. Microabsorption and preferred orientations were included in a controlled way, by choosing the appropriate components each time. The datasets are respectively: - D1: A three component mixture containing bismuth oxide (MM: 208.980 g/mol, density: 8.93 g/cm^3, Z of the heaviest element: 83), barium sulfate (MM: 137.327 g/mol, density: 4.48 g/cm^3, Z of the heaviest element: 56) and sieved graphite (MM: 12.01 g/mol, density: 2.23 g/cm^3, Z of the heaviest element: 6) with a particle size lower than 90 micrometers (non-oriented). - D2: A three component mixture containing bismuth oxide (MM: 208.980 g/mol, density: 8.93 g/cm^3, Z of the heaviest element: 83), barium sulfate (MM: 137.327 g/mol, density: 4.48 g/cm^3, Z of the heaviest element: 56) and sieved graphite (MM: 12.01 g/mol, density: 2.23 g/cm^3, Z of the heaviest element: 6), divided in about 70 % sieved graphite with a particle size lower than 90 micrometers (non-oriented) and 30% sieved graphite with particle size higher than 90 micrometers (oriented). - D3: A three component mixture containing bismuth oxide (MM: 208.980 g/mol, density: 8.93 g/cm^3, Z of the heaviest element: 83), barium sulfate (MM: 137.327 g/mol, density: 4.48 g/cm^3, Z of the heaviest element: 56) and zinc acetate (MM: 219.528 g/mol, density: 1.74 g/cm^3, Z of the heaviest element: 30). - D4: A three component mixture containing bismuth oxide (MM: 208.980 g/mol, density: 8.93 g/cm^3, Z of the heaviest element: 83), barium sulfate (MM: 137.327 g/mol, density: 4.48 g/cm^3, Z of the heaviest element: 56) and urea (MM: 60.06 g/mol, density: 1.32 g/cm^3, Z of the heaviest element: 8). The aim of this datasets is to provide an open-source collection data on which perform quantification analysis by both traditional and innovative methods. More informations are detailed in the data paper "XRF and XRPD data sets in ternary mixtures with high level micro-absorption and/or preferred orientations problems for phase quantification analysis".
DOI: 10.6084/m9.figshare.19500671.v1
title: Record Linkage Datasets

description: This simulated dataset is a corrupted segment from the Social Security Death Master File (SSDMF) available at https://ssdmf.info/. There are 11 original datasets: ``dsxo`` where `x` runs from `1...11` and the suffix `o` stands for `original`. The sizes (number of original records) of these datasets are as follows:<br>| dataset | size ||:----------:|:----:|| ds1o | 10K || ds2o | 20K | | ds3o | 40K || ds4o | 80K || ds5o | 120K || ds6o | 160K || ds7o | 200K || ds8o | 400K || ds9o | 600K || ds10o | 800K || ds11o | 1M |<br>These original records are then corrupted via a modified version of the `dsgen` Python script by `Peter Christen`.The modified/corrupted files are saved as: ``dsxm`` where the suffix `m` stands for `modified`.The modified records plus four original replicates are concatenated and mixed up (by the Linux command tool `shuf`).The resultant datasets are named: ``dsx.0`` ``(dsx.1)`` before(after) shuffling.So, the sizes of these datasets are as follows:<br>| dataset | size ||:-------:|:----:|| ds1.1 | 50k || ds2.1 | 100k || ds3.1 | 200k || ds4.1 | 400k || ds5.1 | 600k || ds6.1 | 800k || ds7.1 | 1M || ds8.1 | 2M || ds9.1 | 3M || ds10.1 | 4M || ds11.1 | 5M |<br>Furthermore, each dataset is split into two halves to serve as input for record linkage algorithms. For example, ds1.1 is split into ds1.1.1 & ds1.1.2.<br>
DOI: 10.6084/m9.figshare.22060985.v1
title: Data and Code for Curveira-Santos et al. "Disturbance-mediated changes to boreal mammal spatial networks in industrializing landscapes"

description: Data and model code used in Curveira-Santos et al. “<em>Disturbance-mediated changes to boreal mammal spatial networks in industrializing landscapes</em>”.
<br>
<br>
<em><strong>i. </strong></em><strong>Data:</strong> “sem_dat.csv” 
<br>
Species detection and covariate data from camera-trap surveys conducted across a three-area disturbance gradient (Richardson [low disturbance], Algar [medium disturbance], Christina lake [high disturbance]) within Canada’s Athabasca Oil Sands Region.
<br>
Rows represent sampling sites (site [camera-trap] by year and by landscape combinations).
<br>
Columns represent species-specific site use intensity data (independent counts at sampling sites) and covariate values for on-/off-line camera placement (on-line), linear feature density (lf1500), block feature cover (bf1500), and wetland probability (wb500).
<br>
<br>
<em><strong>ii.</strong></em><strong> Model Code:</strong> “SEM_Rcode.R”
<br>
Code written for program R and using the <em>piecewiseSEM</em> and <em>lme4 </em>packages to implement the Structural Equation Model.
<br>
DOI: 10.6084/m9.figshare.25378600.v1
title: <i>Mycoplasma anserisalpingitidis</i> genome wide association study data

description: The following contains the background data, the phenotypic data (MIC values of 9 antibiotics) of <i>Mycoplasma anserisalpingitidis</i>. Along the phenotype data the draft genomes of the strains are also present.
DOI: 10.6084/m9.figshare.25943430.v1
title: Egg survival data for experiment 3 with tortuganus egg treatment from Innate and learned components of egg recognition in the ant <i>Camponotus floridanus</i>

description: The data represent the number of surviving eggs
DOI: 10.6084/m9.figshare.29133221.v1
title: Data Science for Environmental Justice PBL Module: Non-Renewable Energy Data

description: This is a PBL module on Non-Renewable Energy to be used in an introductory environmental science course to motivate students to discuss related climate justice issues. (Please note that this module has not yet been tested in the classroom as of May 22, 2025.)Original data was from the U.S. Energy Information Administration under their State Energy Data System (SEDS): 1960-2022 (complete) under the Key statistics and ranking section.This data was processed and cleaned as described in the data provenance document.Lecture Slides, Activity Sheets and Instructor Notes are available here.The following files are included:<b>Data Provenance: </b>EIA-Non-Renewable-Data-Provenance.pdf<b>R Script for Data Processing:</b> DSEJ_data_cleaning_EIA.R<b>Curated Data used in the Module:</b> EIA_Non_Renewable.csv<b>Original Dataset from EIA: </b>use_tot_realgdp.csv
DOI: 10.6084/m9.figshare.29178335.v1
title: Data Factor Marketization;Firms’ Breakthrough Innovation

description: The data of "Data Factor Marketization and Firms’ Breakthrough Innovation:Evidence from the Establishment of Data Trading Platforms in China"
DOI: 10.6084/m9.figshare.26236538.v1
title: 2024_07_10

description: <pre>Additional data of Medicago seed shape to genetics manuscript.<br><br>alfalfa_samtools.merged3.vcf: vcf for GWAS<br>alfalfa_samtools.merged4.txt: genotypic matrix for machine learning methods<br>Alfalfa_seed_size.txt: phenotypic data<br>relative_imp_Shaum.R: R script used for machine learning methods</pre>
DOI: 10.6084/m9.figshare.27896733.v1
title: The Impact of Big Five Personality Traits on Z-Generation Designers' Subscription to Paid AI Drawing Tools

description: This study constructs an extended model based on the Big Five Personality (BFP) model, Technology Acceptance Model (TAM), and Perceived Risk Theory (PRT), using Structural Equation Modeling (SEM) and Fuzzy-Set Qualitative Comparative Analysis (fsQCA) to uncover the complex relationship between personality traits and the willingness of Generation Z designers to pay for AI drawing tools.