cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/yhg5wk39kf
title: Ransomware and user samples for training and validating ML models

description: Ransomware is considered as a significant threat for most enterprises since past few years. In scenarios wherein users can access all files on a shared server, one infected host is capable of locking the access to all shared files. In the article related to this repository, we detect ransomware infection based on file-sharing traffic analysis, even in the case of encrypted traffic. We compare three machine learning models and choose the best for validation. We train and test the detection model using more than 70 ransomware binaries from 26 different families and more than 2500 h of ‘not infected’ traffic from real users. The results reveal that the proposed tool can detect all ransomware binaries, including those not used in the training phase (zero-days). This paper provides a validation of the algorithm by studying the false positive rate and the amount of information from user files that the ransomware could encrypt before being detected.

This dataset directory contains the 'infected' and 'not infected' samples and the models used for each T configuration, each one in a separated folder.

The folders are named NxSy where x is the number of 1-second interval per sample and y the sliding step in seconds.

Each folder (for example N10S10/) contains:
	- tree.py -> Python script with the Tree model.
	- ensemble.json -> JSON file with the information about the Ensemble model.
	- NN_XhiddenLayer.json -> JSON file with the information about the NN model with X hidden layers (1, 2 or 3).
	- N10S10.csv -> All samples used for training each model in this folder. It is in csv format for using in bigML application.
	- zeroDays.csv -> All zero-day samples used for testing each model in this folder. It is in csv format for using in bigML application.
	- userSamples_test -> All samples used for validating each model in this folder. It is in csv format for using in bigML application.
        - userSamples_train -> User samples used for training the models.
        - ransomware_train -> Ransomware samples used for training the models
        - scaler.scaler -> Standard Scaler from python library used for scale the samples.
        - zeroDays_notFiltered -> Folder with the zeroDay samples.

In the case of N30S30 folder, there is an additional folder (SMBv2SMBv3NFS) with the samples extracted from the SMBv2, SMBv3 and NFS traffic traces. There are more binaries than the ones presented in the article, but it is because some of them are not "unseen" binaries (the families are present in the training set).

The files containing samples (NxSy.csv, zeroDays.csv and userSamples_test.csv) are structured as follows:
	- Each line is one sample.
	- Each sample has 3*T features and the label (1 if it is 'infected' sample and 0 if it is not).
	- The features are separated by ',' because it is a csv file.
	- The last column is the label of the sample.

Additionally we have placed two pcap files in root directory. There are the traces used for compare both versions of SMB.
DOI: 10.6084/m9.figshare.16610536.v1
title: COVID-19_Radiography_Dataset

description: This database consists 3,616 COVID-19 positive cases, 6,012 Lung Opacity (Non-COVID lung infection) and 1,345 Viral Pneumonia images. For the original datasets, it is the 2nd update version, and in which three classes (namely, COVID-19, Non-COVID lung infection and Viral Pneumonia) are used in our paper.
DOI: 10.6084/m9.figshare.28530359.v1
title: cryofold_source_data.zip

description: E3-CryoFold source data
DOI: 10.6084/m9.figshare.7871882.v3
title: Matrix

description: MALDI Mass Spec Expression Matrix and Pheno data
DOI: 10.6084/m9.figshare.833848.v1
title: Tau trace of CG class B with 32 processors on Graphene

description: This is a Tau trace of the NASPB CG Benchmark obtained by Georges Markomanolis on the Graphene cluster.
DOI: 10.6084/m9.figshare.25359220.v1
title: <b>Screening of a potential probiotic</b><b><i> Lactiplantibacillus plantarum</i></b><b> NUC08 </b><b>and its synergistic effects with yogurt starter</b>

description: This study aims to identify lactic acid bacteria (LAB) isolates possessing physiological characteristics suitable for use as probiotics in yogurt fermentation. Following acid and bile salt tolerance tests, <i>Lactiplantibacillus</i><i> plantarum </i>(NUC08 and NUC101), <i>Lacticaseibacillus rhamnosus</i><i> </i>(NUC55 and NUC201), and <i>Lacticaseibacillus</i><i> paracasei </i>(NUC159, NUC216 and NUC351) were shortlisted based on intraspecies distribution for further evaluation. Their physiological probiotic properties, including transit tolerance, adhesion, auto-aggregation, surface hydrophobicity, biofilm formation, and antibacterial activity, were assessed. Principal Component Analysis (PCA) indicated that<i> L. plantarum </i>NUC08 was the preferred choice among the evaluated strains. Subsequent investigations revealed that co-culturing <i>L. plantarum</i> NUC08 with two yogurt starter strains resulted in a cooperative and synergistic effect, enhancing the growth of mixed strains and increasing their tolerance to simulated gastric and intestinal conditions. Additionally, when <i>Vibrio harveyi </i>bioluminescent reporter strain was used, the three co-cultured strains cooperated to induce the activity of a quorum sensing (QS) molecule AI-2, hinting a potential connection between phenotypic traits and QS in the co-cultured strains. Importantly, LAB viable counts were significantly higher in yogurt co-fermented with <i>L. plantarum</i> NUC08, consistently throughout the storage period. In conclusion, the study demonstrates that the probiotic strain <i>L. plantarum </i>NUC08 can be employed in synergy with yogurt starter strains, affirming its potential for use in the development of functional fermented dairy products.
DOI: 10.6084/m9.figshare.3517655.v1
title: Appendix D. Coefficients (β) and 95% confidence intervals (CI) from multinomial logistic regression (mlogit) models of mosquito and black fly activity and from logistic regression model of oestrid presence on the Bathurst caribou post-calving and summer range, Northwest Territories and Nunavut, Canada. Coefficients presented are from models selected from the full model sets. Coefficients for growing degree-days (gdd2), light, easting, and northing are scaled by a factor of 1000.

description: Coefficients (β) and 95% confidence intervals (CI) from multinomial logistic regression (mlogit) models of mosquito and black fly activity and from logistic regression model of oestrid presence on the Bathurst caribou post-calving and summer range, Northwest Territories and Nunavut, Canada. Coefficients presented are from models selected from the full model sets. Coefficients for growing degree-days (gdd2), light, easting, and northing are scaled by a factor of 1000.
DOI: 10.6084/m9.figshare.5501539.v1
title: DVS recordign Super Resolution

description: The videos are the results of the super-resolution of DVS recordigns. The file name ended with "_SR" is the super-resolution results.
DOI: 10.17632/dwp2nnfgft
title: One-pot DTECT enables rapid and efficient capture of genetic signatures for precision genome editing and clinical diagnostics. Baudrier et al.

description: Raw data from Baudrier, Benamozig et al.
DOI: 10.6084/m9.figshare.853801_d9
title: SSI_MERRA.zip

description: The Global Integrated Drought Monitoring and Prediction System (GIDMaPS) provides near real-time drought information based on multiple drought indicators and input data sets (http://drought.eng.uci.edu/). In this package, the folder names indicate the drought index and the source of data (INDEX_SOURCE). Index options are: 1-PSI: Multivariate Standardized Drought Index (MSDI) based on Precipitation (P) and Soil Moisture (S). 2-SSI: Standardized Soil Moisture Index 3-SPI: Standardized Precipitation Index. In each folder the files are named as INDEXYYYYMM.asc, where YYYY= year; MM= month, and INDEX = PSI, SSI, SPI.
<br>
GIDMaPS is presented in [Hao et al, 2014]. The deatails of MSDI, SSI and SPI are discussed in [Hao and AghaKouchak 2013a, 2013b]. GIDMaPS data has been used in [Damberg and AghaKouchak, 2013].
 
<strong>References</strong>
[1] Hao Z., AghaKouchak A., Nakhjiri N., Farahmand A., 2014, Global Integrated Drought Monitoring and Prediction System, Scientific Data, 1: 140001, doi: 10.1038/sdata.2014.1.
<br>
[2]  Hao Z., AghaKouchak A., 2013a, A Nonparametric Multivariate Multi-Index Drought Monitoring Framework,Journal of Hydrometeorology, doi:10.1175/JHM-D-12-0160.1.
<br>
[3]  Hao Z., AghaKouchak A., 2013b, Multivariate Standardized Drought Index: A Parametric Multi-Index Model,Advances in Water Resources, 57, 12-18, doi: 10.1016/j.advwatres.2013.03.009.
<br>
[4]  Damberg L., AghaKouchak A., 2013, Global Trends and Patterns of Droughts from Space, Theoretical and Applied Climatology, doi: 10.1007/s00704-013-1019-5. 
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DOI: 10.17632/tdggxgygrw
title: Sequencing data from human brain metastases

description: Processed scRNA-seq and spatial transcriptomics for Sudmeier et al., Cell Reports Medicine 2022. "Metacluster signatures.xlsx" shows the gene expression profile for each metacluster of brain metastasis-infiltrating PD-1+ CD8+ T cell. "CellphoneDB output.xlsx" contains the CellphoneDB output from spatial transcriptomics experiments.
DOI: 10.6084/m9.figshare.4659565.v6
title: Cranial Defect Datasets

description: Cranial Defect Datasets<br>Please use the following citations if you use them in your work:<br>M. Gall, et al. Cranial Defect Datasets. Figshare, 2017.<br><br>and<br>J. Egger, M. Gall, A. Tax, M. Ücal, U. Zefferer, X. Li, G. von Campe, U. Schäfer, D. Schmalstieg, X. Chen. “Interactive reconstructions of cranial 3D implants under MeVisLab as an alternative to commercial planning software“. In: PLoS ONE, 12(3): e0172694, 2017.https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0172694<br><br>
DOI: 10.6084/m9.figshare.7890935.v1
title: Spatial Data and Python Code for 13 Development Potential Indices (part 04)

description: Last of four zipfiles providing all data and Python code necessary to replicate any of the 13 development potential indexes (DPIs) described within Oakleaf et al. (2019), “Mapping global development potential for renewable energy, fossil fuels, mining and agriculture sectors”. A README.pdf guides users on setting up environment necessary to use data and run Python code. To run Python code with accompanying spatial data, 64 GBs of disk space is required. Additionally ArcPY, a python module associated with ESRI’s ArcGIS Desktop, and an accompanying Spatial Analyst extension license are required to run Python code. All code was created by J.R. Oakleaf during 2018 and is licensed under Creative Commons Attribution-NonCommercial 4.0 International License http://creativecommons.org/licenses/by-nc/4.0/.
DOI: 10.17632/v2wr7grbbg
title: Novel domestic building energy consumption dataset: 1D timeseries and 2D Gramian Angular Fields representation

description: This dataset, collected in 2022 in a domestic household in the UK, provides appliance-level power consumption data and ambient environmental conditions as a timeseries and as a collection of 2D images created using Gramian Angular Fields (GAF). The importance of the dataset lies in (a) providing the research community with a dataset that combines appliance-level data coupled with important contextual information for the surrounding environment; (b) presents energy data summaries as 2D images to help obtain novel insights from the data using data visualization and Machine Learning (ML). The methodology involves installing smart plugs to a number of domestic appliances, environmental and occupancy sensors, and connecting the plus and the sensors to a High-Performance Edge Computing (HPEC) system to privately store, pre-process, and post-process data. The heterogenous data include several parameters, including power consumption (W), voltage (V), current (A), ambient indoor temperature (°C), relative indoor humidity (RH%), and occupancy (binary). The dataset also includes outdoor weather conditions  based on data from MET Norway including temperature (°C), outdoor humidity (RH%), barometric pressure (hPA), wind bearing (deg), and windspeed (m/s). This dataset is valuable for energy efficiency researchers, electrical engineers, and computer scientists to develop, validate, and deploy and computer vision and data-driven energy efficiency systems.
DOI: 10.6084/m9.figshare.21790061.v2
title: Clustering files iteration 2

description: Clustering files for each mixture iteration 2
DOI: 10.6084/m9.figshare.5802720.v2
title: Machine Learning Based Localization and Classification with Atomic Magnetometers - Shape and Position

description: Datasets created with an atomic magnetometer performing magnetic induction imaging, and later analysed with machine learning.<br>In this dataset, object's position is randomly varied in the (x,y) plane, and four different shapes are used.Shape 01: 50 mm x 50 mm x 3 mm Aluminium squareShape 02: 50 mm diameter x 3 mm Aluminium diskShape 03: 50 mm x 25 mm x 3 mm Aluminium rectangleShape 04: 50 mm base isosceles triangle, 50 mm height.Data are arranged per shape; position is indicated in the file's name, in mm.Four classes of data are collected: Radius (R), Phase (P), in-phase (X), and out-of-phase (Y). <br>Blind datasets: Information removed from filename.Details are reported in the file: Shape_Position_blind_files.pdf<br>For further details, see the paper published in Physical Review Letters: https://doi.org/10.1103/PhysRevLett.120.033204.
DOI: 10.17632/5z3k4cmtnr
title: On bootstrapping, data overfitting and crocodiles

description: Data and code for the article "On bootstrapping, data overfitting and crocodiles: : an additional comment to McPherron et al (2022)".
DOI: 10.17632/jgmg26k6jm
title: self-sacrificial leadership

description: data for Sacrifice self-interest, serve the greater good: How does self-sacrificial leadership breed hospitality employees’ OCB?
DOI: 10.6084/m9.figshare.13060325.v1
title: Supplementary Material for: Efficacy and Feasibility of Magnifying Blue Laser Imaging without Biopsy Confirmation for the Diagnosis of the Demarcation of Gastric Tumors: A Randomized Controlled Study

description: <b><i>Background:</i></b> Accurate diagnosis of the demarcation line (DL) of gastric tumors is essential for curative complete resection by endoscopic submucosal dissection (ESD). It is controversial to perform only magnifying endoscopy for diagnosing the DL of gastric tumors prior to ESD. This study aimed to evaluate the diagnostic accuracy for the DL of gastric adenomas and well-differentiated adenocarcinomas using only magnifying blue laser imaging (M-BLI) compared with that using both M-BLI and biopsy confirmation. <b><i>Methods:</i></b> In this prospective, single-center study, 96 well-differentiated adenocarcinomas and 32 gastric adenomas were enrolled between July 2015 and December 2016. A total of 122 lesions with a clear DL on M-BLI were randomly allocated to undergo M-BLI only (the M-BLI group) or M-BLI with biopsy confirmation (the M-BLI-BC group), performed as biopsies in 4 directions from noncancerous tissues ≈ 5 mm outside the lesion before ESD. The primary end point was to clarify the noninferiority of M-BLI without biopsy confirmation compared with that with biopsy confirmation, in terms of the diagnostic accuracy and complete resection. <b><i>Results:</i></b> There were no significant differences in sex, median age, color, circumference, macroscopic type, biopsy-based diagnosis, and <i>Helicobacter pylori</i> infection between the 2 groups. The diagnostic accuracy for the DL was 100 and 95.0% and the complete resection was 100 and 100% in the M-BLI and M-BLI-BC groups, respectively. <b><i>Conclusion:</i></b> The diagnostic ability of M-BLI is excellent in diagnosing the demarcation of gastric adenoma and well-differentiated adenocarcinoma. Biopsy confirmation is not needed for these lesions with a clear DL by M-BLI.
DOI: 10.6084/m9.figshare.7937246.v6
title: Cancer Datasheets

description: Cancer datasheets for classification of cancer samples based on tumor immune phenotype according to expression of the Immunologic Constant of Rejection gene signature.
DOI: 10.6084/m9.figshare.20188670.v1
title: data_analysis_TracerMixing.jld2

description: This file contains the data analysis of an ensemble of 50 tracer release experiments. The analysis was done for the paper "Can we accurately quantify a lateral diffusivity from a single tracer release?" All data presented in this paper can be found in this file.
DOI: 10.25452/figshare.plus.22277626.v1
title: Physiological indices directory

description: <b>Directory content</b>: This directory contains 21 .CSV files with electrodermal activity (EDA), electrocardiogram (ECG), blood volume pulse (BVP), and respiration data (recorded at 400 Hz) for each driver (i.e., ID_Number_BiosignalsData). All files were pre-processed in two steps to ensure the correct sampling rate: (i) removing lines with timestamps repetitions, and (ii) resampled to 400 Hz using regularly spaced and linear interpolation. This directory also includes a .CSV file reporting the data description (Legend_DataBiosignal).<b>Method and instruments</b>: We used a BiosignalsPlux Research Kit (PLUX Wireless Biosignals, Lisbon, Portugal) to monitor participants’ ECG, BVP, EDA, and respiration data. The BiosignalsPlux system includes a wearable hub with an 8-channel configuration (analog ports) of 16-bit per channel resolution, using Bluetooth data transmission technology for synchronization with the driving simulator.We used disposable, self-adhesive, pre-gelled Ag/AgCl electrodes (24 mm diameter) for the ECG and EDA measurements. The EDA was recorded employing a dedicated single-lead local differential bipolar DC sensor (0-3 Hz bandwidth, 0-100 µS range), with two leads (a positive and a negative lead, 5.0 ± 0.5 cm length each), each one ending with a dedicated electrode socket. Once we cleaned the skin with an alcohol-free disinfectant, we placed the electrodes on the thenar (negative electrode) and hypothenar (positive electrode) eminences of the left hand. We made sure to let enough space on the hand palm between the two electrodes to minimize the risk of signal artifacts due to the pressure of the hand on the steering wheel. The ECG was recorded with a single-lead local differential bipolar sensor (0.5-100 Hz bandwidth, ± 1.47 mV range), including a positive, a negative, and a reference cable, each one ending with a dedicated electrode socket. Once we cleaned the skin, we placed the electrodes on the participant’s chest (Lead II configuration): one electrode on the depression below each of the shoulder blades (reference on the left side, positive on the right side) and one electrode (negative) on the fifth intercostal space of the left side.The BVP was measured through an optical, non-invasive ear-clip sensor (0.02-2.1 Hz bandwidth, 535±10 nm centroid wavelength), including a light emitter (LED) and detector. The sensor (LED and detector) was placed at the center of the left ear lobe.The respiration data was recorded using an elastic, adjustable chest belt that included a piezoelectric sensor (0.059-1 Hz bandwidth, ± 1.50 V range). We placed the belt on the participant’s chest, over a cotton short-sleeve T-shirt, ∼2 cm below the pectoral muscles, and connected to the hub using a dedicated cable of about 110 cm total length.
DOI: 10.6084/m9.figshare.25909813.v1
title: A semi-labelled dataset for fault detection in air handling units from a large-scale office

description: Fault detection and diagnosis (FDD) in AHUs within HVAC systems ensure optimal performance, energy efficiency, and occupant comfort by quickly identifying and diagnosing faults. Combining deep learning with FDD has demonstrated high generalization ability in this field. To develop deep learning models, this research constructed a dataset sourced from real data collected from a large-scale office in South Korea. The raw HVAC data were extracted from a building management system (BMS) at 1 hour interval, spanning from July to October 2023. The dataset was partially labeled by annotation experts, categorizing the data into four types: normal, fan fault, sensor fault, and valve fault. The main contributions of this dataset to the field are twofold. First, it represents a unique dataset sourced from the real operational data of a large-scale office, which is currently non-existent in this domain. Second, the dataset's expert labeling adds significant value by ensuring accurate fault classification. Therefore, we hope this dataset will encourage the development of robust FDD techniques that are more suitable for real-world applications.
DOI: 10.6084/m9.figshare.28490204.v2
title: Joint Super-Resolution and Segmentation for 1-m Impervious Surface Area Mapping in China’s Yangtze River Economic Belt

description: <b><i>High-resolution impervious surface area (ISA) mapping is critical for a wide range of applications, including sustainable urban planning, flood risk assessment, and land use monitoring. However, the production of meter ISA products has long relied on commercial very high-resolution (VHR) satellite imagery, which is cost-prohibitive and geographically limited. To this end, we propose a novel joint framework by integrating super-resolution and segmentation, called JointSeg, which enables the generation of 1-meter ISA maps directly from freely available Sentinel-2 imagery (10m resolution). JointSeg was trained on multimodal cross-resolution inputs, offering a scalable and affordable alternative to traditional approaches. This synergistic design enables gradual resolution enhancement from 10m to 1m while preserving fine-grained spatial textures, and ensures high classification fidelity through effective cross-scale feature fusion. This method has been successfully applied to the Yangtze River Economic Belt (YREB), a region characterized by complex urban–rural patterns and diverse topography. As a result, a comprehensive ISA mapping product for 2021, referred to as ISA-1, was generated, covering an area of over 2.2 million square kilometers. Quantitative comparisons against the 10m ESA WorldCover and other benchmark products reveal that ISA-1 achieves an F1-score of 85.71%, outperforming bilinear-interpolation-based segmentation by 9.5%, and surpassing other ISA datasets by 21.43%-61.07%. In densely urbanized areas (e.g., Suzhou, Nanjing), ISA-1 reduces ISA overestimation through improved discrimination of green spaces and water bodies. Conversely, in mountainous regions (e.g., Ganzi, Zhaotong), it identifies significantly more ISA due to its enhanced ability to detect fragmented anthropogenic features such as rural roads and sparse settlements, demonstrating its robustness across diverse landscapes. Moreover, we present biennial ISA maps from 2017 to 2023, capturing spatiotemporal urbanization dynamics across representative cities. The results highlight distinct regional growth patterns: rapid expansion in upstream cities, moderate growth in midstream regions, and saturation in downstream metropolitan areas. These findings underscore the framework’s utility for continuous, high-resolution urban monitoring using only open-access data. In summary, our method establishes a transformative approach to ISA mapping by overcoming the resolution limitations of medium-resolution imagery. This technique opens new possibilities for large-scale, fine-resolution ISA monitoring without the dependency on expensive VHR data, thus supporting global efforts in sustainable development and urban resilience planning.</i></b>