cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.27908904.v1
title: DATA_Perception of tactile disgust - the role of object characteristics, sensitivity to different disgust subscales and fear

description: This dataset contains data from 113 participants, collected in three individual runs, one in Olomouc, Czech Republic (S1, n = 32) in April 2022, and two in Prague, Czech Republic, in June 2022 (S2, n = 51) and in October 2022 (S3, n = 30). The participants underwent an experimental task. In this task, they were asked to touch eight objects hidden in boxes, rating their tactile disgust upon touching the object, disgust at the thought of putting the object in their mouth, and the likelihood of falling sick if the object was licked or consumed, on a scale from 1 (not at all/none) to 7 (very/high). They were also asked to rate their feeling of fear while touching the object, on a scale 1 (not at all) to 5 (very), and object characteristics (slimy, wet, viscous/sticky, lumpy, brittle and greasy), on a scale from 1 (not at all) to 6 (very). The objects were: O1: Tapioca (soaked and drained), O2: Coconut flakes, O3: Molasses on baking paper with dried fruits, O4: Towel (control object), O5: Bathroom tile with dried shaving cream, O6: Runny oatmeal with pieces of pear, O7: Wet Play-Doh in an excrement shape, O8: Mayonnaise. Participants also completed the Disgust Scale-Revised, the Three Domains of Disgust Scale (Pathogen domain), and the Perceived Vulnerability to Disease questionnaire; a subsample (N = 57) completed the Culpepper Disgust Image Set.<br><br>The dataset was used for the analysis of tactile disgust in relation to existing disgust measures and the objects' characteristics and how fear could influence these relationships. In the analyses, we used correlations (wide format dataset) and mixed models (long format dataset).
DOI: 10.6084/m9.figshare.28380977.v1
title: Accurate Through-Wall Breathing Rate Measurements using a C-Band FMCW Radar

description: This dataset presents a collection of through-wall breathing rate (BR) measurements obtained using a custom-built C-Band Frequency Modulated Continuous Wave (FMCW) radar operating within the 4–6 GHz frequency range. The dataset was created as part of a research study on non-invasive biometric identification through physical barriers and is particularly valuable for researchers working on radar-based vital sign monitoring, signal processing, and artificial intelligence (AI) models applied to biomedical applications.The dataset consists of breathing rate measurements from 10 individuals, including 5 males and 5 females. The radar signals were recorded through a 30 cm thick brick and cement wall at distances of 1m, 3m, and 4m. The data collection process was carefully designed to ensure high accuracy, with ground truth validation provided by the Biopac system (MP36R). The radar system used a center frequency of 5 GHz, a bandwidth of 2 GHz, a transmitted power of 20 dBm, and an antenna gain of 12 dBi. The chirp duration was set to 1 ms, and the sampling rate was 1 Msps, ensuring high-resolution signal acquisition.The dataset includes raw radar signals (intermediate frequency signals), extracted phase signals for breathing rate estimation, corresponding ground truth breathing rates from the Biopac system, and both time-domain and frequency-domain processing results. These data points make the dataset valuable for applications such as radar-based biometric identification, through-wall sensing and security applications, healthcare monitoring with contactless breathing rate detection, AI-based signal processing for vital signs extraction, and the optimization of radar signal processing techniques.In terms of performance, the FMCW radar system demonstrated high accuracy when compared to the Biopac reference measurements, achieving a Mean Absolute Error (MAE) of 0.07 BPM and a Root Mean Square Error (RMSE) of 0.09 BPM. Agreement analysis showed a strong correlation (R² ≈ 0.999) between the radar-based measurements and the ground truth data, confirming the reliability of the system for through-wall breathing rate detection.This dataset was collected in compliance with ethical guidelines approved by the Ethics and Deontology Committee of the University of Aveiro (No. 10-CED/2023). All participants provided informed consent before data acquisition. The dataset serves as a valuable resource for researchers developing advanced non-contact monitoring systems and exploring new methodologies in radar-based physiological sensing.
DOI: 10.6084/m9.figshare.28465103.v1
title: Global vegetation cover disturbance change dataset in 2000-2020 (part 3:206-302)

description: This dataset describes the types of changes in different vegetation, mainly involving four vegetation types: forest, shrub, grassland, and cropland. Nine main types of vegetation disturbance were identified for the four vegetation types: farmland expansion or occupation (Type 1), afforestation/conversion of farmland to forests/secondary forests, etc. (Type 2), forest degradation/fallow/newly shrubs/artificial shrubs (Type 3), deforestation/shrub degradation/fallow (Type 4), built-up expansion or occupation (Type 5), vegetation degradation to bare land/fire (Type 6), water flooding into wetlands (Type 7), water flooding/artificial water bodies (Type 8), and vegetation expansion (Type 10). The Spatial resolution of this dataset is 30m (due to factors such as cloud and image quality, some area's spatial resolution is resampled to 40m).To verify the production accuracy of the global vegetation cover disturbance change dataset, we randomly selected 2425 sample points worldwide. These sample points cover different vegetation types: cropland (615), forest (617), shrub (762), grassland (431). Meanwhile, these sample points also cover 9 main types of vegetation disturbance: Type 1（189）、Type 2（197）、Type 3（195）、Type 4（212）、Type 5（279）Type 6（282）、Type 7（319）、Type 8（308）、Type 10（444）。The overall accuracy of the dataset is as high as 85%. The accuracy of most types of disturbances is close to or exceeds 80%. Among them, the accuracy such as cropland, shrubs, grasslands, built-up area, bare land, wetlands, and water bodies were 84%, 83%, 77%, 83%, 85%, 87%, and 87%, respectively. In contrast, the accuracy of vegetation expansion type disturbance is as high as 92%.
DOI: 10.6084/m9.figshare.28883489.v3
title: Parkinson_data.xlsx

description: The dataset comprises baseline chracteristic of Parkinson's disease patients (53) and healthy subject (47). A file, including tapping feature on finger tapping that extract by computer vision.The tapping signal:Tapping Score: rp_count, lp_countTapping Difference: tap_diffMean Tapping Amplitude: rp_mean, lp_meanTapping Variation: rp_std, lp_stdAmplitude Decrement 5: rp_diff5, lp_diff5Amplitude Decrement 10: rp_diff10, lp_diff10
DOI: 10.6084/m9.figshare.29162330.v1
title: <b>Empathic Concern Drives Pro-sociality Following Social Exclusion: The Moderating Role of Social Distance</b>

description: A large body of research has demonstrated that social exclusion undermines empathy and prosocial behavior, with empathy serving as a key motivator of prosocial actions. However, it remains unclear which specific component plays a central role in the link between social exclusion and prosocial behavior. In Study 1, it was found that cognitive empathy and empathetic concern mediated the relationship between social exclusion and prosocial behavior tendencies. Additionally, according to the goal-driven resource allocation theory, individuals determine whether to engage in prosocial behavior based on the likelihood that their need to belong will be fulfilled, with social distance being one key criterion for this judgment. Therefore, Study 2 examined how social distance moderates the relationships among social exclusion, empathy, and prosocial behavior. Results showed that prosocial behavior was promoted in close social distance conditions, but inhibited in distant social distances, due to a reduction in empathetic concern. These findings highlight the pivotal role of empathetic concern in the link between social exclusion and prosocial behavior and demonstrate that social distance moderates this effect.
DOI: 10.17632/xpy2rmgss7
title: Low Cost Insulin Pump

description: Design files for the Open Source Insulin Pump project, completed by UC Bioengineering at University of Canterbury.
DOI: 10.25452/figshare.plus.19310870.v1
title: Training Cohort Slide Images for "Intratumoral resolution of driver gene mutation heterogeneity in renal cancer using deep learning"

description: This item contains whole slide images (in SVS format) of all samples from the Training cohort analyzed in the paper "Intratumoral resolution of driver gene mutation heterogeneity in renal cancer using deep learning" by Acosta et al in Cancer Research (https://doi.org/10.1158/0008-5472.CAN-21-2318). This work demonstrates that deep learning (DL) models can predict the intratumor heterogeneity in driver mutation status purely from Hematoxylin and Eosin (H&E) stained slides. <br> Specifically, we trained and validated DL models that predict the status of three of the most frequently mutated driver genes (BAP1, PBRM1, and SETD2) in clear cell renal cell carcinoma. The DL models were trained on a large cohort of whole slide images (N=1282, referred to as WSI cohort in the paper/code) and tested on several independent cohorts including the TCGA KIRC (N=363 patients), two human tissue microarray (TMA) cohorts (referred to as TMA1 with 118 patients and TMA2 with 365 patients respectively) and a patient-derived xenograft TMA (referred to as PDX1). <br> The current dataset contains the H&E stained whole slide images for the WSI cohort. <br> See related materials in collection at: https://doi.org/10.25452/figshare.plus.c.5983795
DOI: 10.6084/m9.figshare.11597808.v1
title: MOESM1 of Effects of fungal infection on the survival of parasitic bat flies

description: Additional file 1: Table S1. Collection data (sex, location, date and infection status) and survival time of Nycteribia schmidlii and Penicillidia conspicua.
DOI: 10.6084/m9.figshare.6853799.v1
title: Supplementary Material for: Management of Spontaneous Pneumothorax and Post-Interventional Pneumothorax: German S3 Guideline

description: In Germany, 10,000 cases of spontaneous pneumothorax are treated inpatient every year. The German Society for Thoracic Surgery, in co-operation with the German Society for Pulmonology, the German Radiological Society, and the German Society of Internal Medicine has developed an S3 guideline on spontaneous pneumothorax and post-interventional pneumothorax moderated by the German Association of Scientific Medical Societies. <b><i>Method:</i></b> Based on the source guideline of the British Thoracic Society (2010) for spontaneous pneumothorax, a literature search on spontaneous pneumothorax was carried out from 2008 onwards, for post-interventional pneumothorax from 1960 onwards. Evidence levels according to the Oxford Center for Evidence-Based Medicine (2011) were assigned to the relevant studies found. Recommendations according to grade (A: “we recommend”/“we do not recommend,” B: “we suggest”/“we do not suggest”) were determined in 3 consensus conferences by the nominal group process. <b><i>Results:</i></b> The algorithms for primary and secondary pneumothorax differ in the indication for CT scan as well as in the indication for chest drainage application and video-assisted thoracic surgery. Indication for surgery is recommended individually taking into account the risk of recurrence, life circumstances, patient preferences, and procedure risks. For some forms of secondary pneumothorax, a reserved indication for surgery is recommended. Therapy of post-interventional spontaneous pneumothorax is similar to that of primary spontaneous pneumothorax. <b><i>Discussion:</i></b> The recommendations of the S3 Guideline provide assistance in managing spontaneous pneumothorax and post-interventional pneumothorax. Whether this will affect existing deviant diagnostic and therapeutic measures will be demonstrated by future epidemiological studies.
DOI: 10.6084/m9.figshare.9902636.v2
title: Krater_et_al_2020_Data.zip

description: Datasets corresponding to publication:<br>AIDeveloper: deep learning image classification in life science and beyond<br>
DOI: 10.17632/s34pwpv4dr
title: Control of Droplet Impact through Magnetic Actuation of Surface Microstructures

description: An effective method for on-demand control over the impact dynamics of droplets on a magnetoresponsive surface is reported. The surface is comprised of micrometer-sized lamellas from a magnetoactive elastomer on a copper substrate. The surface itself is fabricated using laser micromachining. The orientation of the lamellae is switched from edge-on (orthogonal to the surface) to face-on (parallel to the surface) by changing the direction of a moderate (&lt; 250 mT) magnetic field. This simple actuation technique can significantly change the critical velocities of droplet rebound, deposition, and splashing. Rebound and deposition regimes can be switched up to Weber number We &lt; 13±3, while deposition and splashing can be switched in the range of 32 &lt; We &lt; 52. Because a permanent magnet is used, no permanent power supply is required for maintaining the particular regime of droplet impact. The presented technology is highly flexible and enables selective fabrication and actuation of microstructures on complex devices. It has great potential for applications in soft robotics, microfluidics, and advanced thermal management. 

The following video files show typical water droplet impacts on different surfaces:
• Droplet deposition on flat MAE at We = 10 (flat_We10.avi)
• Droplet deposition on flat MAE at We = 44 (flat_We44.avi)
• Droplet circular splash on flat MAE at We = 100 (flat_We100.avi)
• Droplet deposition on “face on” MAE at We = 10 (side view: face_on_We10(side).avi, front view: face_on_We10(front).avi)
• Droplet deposition on “face on” MAE at We = 44 (side view: face_on_We44(side).avi, front view: face_on_We44(front).avi)
• Droplet parallel splash on “face on” MAE at We = 76 (side view: face_on_We76(side).avi, front view: face_on_We76(front).avi)
• Droplet circular splash on “face on” MAE at We = 100 (side view: face_on_We100(side).avi, front view: face_on_We100(front).avi)
• Droplet rebound on “edge on” MAE at We = 10 (side view: edge_on_We10(side).avi, front view: edge_on_We10(front).avi)
• Droplet penetration on “edge on” MAE at We = 17 (side view: edge_on_We17(side).avi, front view: edge_on_We17(front).avi)
• Droplet parallel splash on “edge on” MAE at We = 44 (side view: edge_on_We44(side).avi, front view: edge_on_We44(front).avi)
• Droplet circular splash on “edge on” MAE at We = 172 (side view: edge_on_We172(side).avi, front view: edge_on_We172(front).avi)
DOI: 10.6084/m9.figshare.15047403.v2
title: Energy transfer from PSI-generated M1 subharmonic waves to high-frequency internal waves

description: This item includes the datasets utilized in the article for submission to Geophysical Research Letters
DOI: 10.6084/m9.figshare.19158425.v3
title: strain_adsorption_data_GNN.tar.gz

description: Dataset of binary copper alloy strained and DFT relaxed catalyst + adsorbate structures, covering 27 small molecule adsorbates. <br>Data is based on the Open Catalyst Dataset
DOI: 10.6084/m9.figshare.24548584.v1
title: Lifetime by CLIMAT

description: File contains lifetimes of electrons and holes found by COnstant light-induced magnetotransport in various materials: p-Silicon3CAT-peroCdTeY6NaBiS2sc-CsPbBr3sc-MAPbBr3FASnI32PACzCuSCNPDINN4H-SiC<br>
DOI: 10.17632/fdfvxx5vjg
title: Data for: A Generative Adversarial Network Approach to Predicting Postoperative Appearance after Orbital Decompression Surgery for Thyroid Eye Disease

description: Dataset file including both preoperative and matched postoperative faces that have undergone orbital decompression surgery. (Updated 2020-01-03)
DOI: 10.6084/m9.figshare.12988919.v1
title: Dataset 2

description: The expression of N0.
DOI: 10.6084/m9.figshare.13348064.v1
title: BICCN Mouse MOp meta-markers

description: We extracted replicable markers for neuron cell types from a compendium of 7 scRNAseq datasets generated by the BICCN in the mouse primary motor cortex (https://doi.org/10.1101/2020.02.29.970558). The markers were extracted using the MetaMarkers package (https://github.com/gillislab/MetaMarkers) using default parameters and keeping the top 1000 markers for each cell type.<br>We present markers for cell types at each level of the hierarchy defined by the BICCN. - biccn_class_markers.csv: highest level with only 3 cell types (excitatory neurons, inhibitory neurons, non-neurons). - biccn_subclass_markers.csv: intermediate level containing 13 cell types (e.g. PV+ interneurons, L6b excitatory neurons). - biccn_cluster_markers.csv: high-resolution level containing 86 cell types (e.g. Chandelier cells).<br>The number of informative markers varies by cell type. To find the best number of markers, we looked for optimal annotation performance for each cell type. The results are summarized in "optimal_number_markers.csv". For each cell type, performance is reported in column "f1" (0.75 indicates good performance, 1 is perfect performance). To read this table, pick a cell type of interest, find optimal performance, then the range of genes that lead to optimal performance. For example, Chandelier cells (Pvalb Vipr2_2) are perfectly characterized by 50 to 200 markers (F1&gt;0.99).
DOI: 10.6084/m9.figshare.13859309.v1
title: Diabetes Awareness, Treatment, and Control among Mexico City Residents

description: Diabetes Awareness, Treatment, and Control among Mexico City ResidentsData base and do file for STATA v15
DOI: 10.6084/m9.figshare.18834620.v3
title: Ngoc Do_Venetoclax plus Obinutuzumab vs Chlorambucil plus Obinutuzumab_Cost-Effectiveness Analysis Model.xlsm

description: The excel model details all the assumptions, data inputs, data sources, and data analysis codes used to output the results represented in our article titled: "Cost-effectiveness of Venetoclax plus Obinutuzumab versus Chlorambucil plus Obinutuzumab for the first-line treatment of adult patients with chronic lymphocytic leukemia - an extended social view."<br><br>Please see the Data appendix file to view detailed description of the model.
DOI: 10.6084/m9.figshare.6275315.v1
title: Quantification of SG formation in the mutants (ts library and SGA-V2) from high-content screenings.xlsx

description: Quantification of SG formation in the mutants (ts library and SGA-V2) from high-content screenings
DOI: 10.17632/6dtgd686rm
title: Data for: Relative sea-level changes and organic matter enrichment in the Upper Ordovician-Lower Silurian Wufeng-Longmaxi Formations in the Central Yangtze area, China

description: Table S1 TOC, major elements content and trace elements content of shale samples from the well EYY2
DOI: 10.6084/m9.figshare.19501444.v2
title: evolution data

description: data set for the evolution study<br>
DOI: 10.6084/m9.figshare.7628642.v1
title: On the Effectiveness of Manual and Automatic Unit Test Generation: Ten Years Later

description: Dataset used for the study
DOI: 10.6084/m9.figshare.12553922.v1
title: Raw data file_small berries.xlsx

description: raw data set
DOI: 10.6084/m9.figshare.13176029.v1
title: Whole-genome resequencing reveals genetic characteristics of Guangxi ducks-Supplementary Files

description: The Supplementary Files of "Whole-genome resequencing reveals genetic characteristics of Guangxi ducks", including figures and tables.Figure S1: Geographic distribution and appearances of typical male ducks. Figure S2: The distribution of SNPs within 1Mb window size over the whole genome.Figure S3: GO and KEGG in Guangxi indigenous duck populations and meat breeds. Figure S4: The ZFst and Log2(θπ ratios) of GX vs egg breeds.Table S1: The geographic origins and abbreviations of 125 ducksTable S2: The information of data availabilityTable S3: Preliminary statistics of whole-genome resequencing databaseTable S4: The distribution of SNPs of the 12 duck populations over the genome by SNPEffTable S5: The distribution of Indels of the 12 duck populations over the genome by SNPEffTable S6: Genetic diversity of the indigenous duck populations in Guangxi and the other 4 duck breeds.Table S7: The Fst between 8 Guangxi indigenous populations, 2 meat and 2 egg breeds.Table S8: The annotated genes between Guangxi ducks and meat breeds.Table S9: The GO and KEGG pathway of GX vs meat breeds.Table S10: The annoted genes between Guangxi ducks and egg breeds.Table S11: The GO and KEGG pathway of GX vs egg breeds.
DOI: 10.17632/w2y9dccpvx
title: Data for Fossil fuel demand elasticities in Iran: diesel, gasoline, fuel oil, Liquefied Petroleum Gas (LPG), and kerosene using ARDL

description: ******************** see README file ********************

Title: Data for Energy efficiency and price policy for sustainable development in Iran: Fossil fuel demand elasticities and sustainability
Version: 1.0
Identifier: doi: 10.17632/w2y9dccpvx.1
Permanent link: http://dx.doi.org/10.17632/w2y9dccpvx.1

Contact Information:
1- Vahid Mohamad Taghvaee; Department of Economic Development and Planning, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran; V.TAGHVAEE@modares.ac.ir
2- Abbas Assari Arani; Department of Economic Development and Planning, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran; ASSARI_A@modares.ac.ir

This directory contains the following dataset and supplementary materials:

1- Fossil fuel demand elasticities in Iran: Required data for estimation of fossil fuels demand elasticities in Iran (including 5 Excel files for the various fossil fuels: diesel, gasoline, fuel oil, LPG, and kerosene)
2- Countries: price and consumption of fossil fuel in various countries in the world
3- EViews file: required data and estimations of the fossil fuel demand model in Iran in EViews Software work file
4- Effects of policies (Environment, Health, Economy): effects of energy efficiency and price policies on environment, health, economy
5- Models: required data for the estimation of fossil fuel demand elasticities in Iran
6- Appendix: methodology for estimating the elasticities in details

Sources of the data are inside the files
DOI: 10.6084/m9.figshare.3563103.v1
title: Appendix A. Detailed description of the simulation study.

description: Detailed description of the simulation study.