cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/wm83krbf32.1
title: HoloSelecta Dataset

description: This dataset contains 295 labelled images for computer vision based detection and identification of packaged products in a vending machine. In addition, the dataset contains data from a user study with 60 participants on the impact of mixed reality headset mediated interventions on food choices. The results indicate that the display of Nutri-Score during the product selection process can alter decisions towards healthier choices. References: * Fuchs, K., Grundmann, T., Fleisch, E., Towards Identification of Packaged Products via Computer Vision, in The 9th International Conference on the Internet of Things (IoT 2019), Bilbao, Spain. * Fuchs, K., Grundmann, T., Haldimann, M., Fleisch, E., Impact of Mixed Reality Food Labels on Product Selection: Insights from a User Study using Headset-mediated Food Labels at a Vending Machine, in 5th International Workshop on Multimedia Assisted Dietary Management In conjunction with the 27th ACM International Conference on Multimedia (ACMMM2019).
DOI: 10.17632/6vdfr627ny.1
title: Funchain et al JAAD 2024 Supplementary

description: Supplementary data for Funchain et al JAAD 2024
DOI: 10.17632/srrt2y5s4m.1
title: Universally Adaptable Multiscale Molecular Dynamics (UAMMD). A native-GPU software ecosystem for complex fluids, soft matter, and beyond

description: We introduce UAMMD (Universally Adaptable Multiscale Molecular Dynamics), a novel software infrastructure tailored for mesoscale complex fluid simulations on GPUs. The UAMMD library encompasses a comprehensive range of computational schemes optimized for the GPU, spanning from molecular dynamics to immersed boundary fluctuating-hydrodynamics. Developed in CUDA/C++14, this header-only open-source software serves both as a simulation engine and as a library with a modular architecture, offering a vast array of independent modules, categorized as interactors (neighbor search, bonded, non-bonded and electrostatic interactions, etc.) and integrators (molecular dynamics, dissipative particle dynamics, smooth particle hydrodynamics, Brownian hydrodynamics and a rather complete array of Immersed Boundary -IB- schemes). UAMMD excels in schemes that couple particle-based elastic structures with continuum fields in different regions of the mesoscale. To that end, thermal fluctuations can be added in physically consistent ways, and fast modes can be eliminated to adapt UAMMD to different regimes (compressible or incompressible flow, inertial or Stokesian dynamics, etc.). Thus, UAMMD is extremely useful for coarse-grained simulations of nanoparticles, and soft and biological matter (from proteins to viruses and micro-swimmers). Importantly, all UAMMD developments are hand-to-hand validated against experimental techniques, and it has proven to quantitatively reproduce experimental signals from quartz-crystal microbalance, atomic force microscopy, magnetic sensors, optic-matter interaction and ultrasound.
DOI: 10.17632/4p77jfkvzn.1
title: Work Stress Data

description: Stress related factors to work design across 5 levels namely psychological, physical, social, organizational and empowerment have been measured through the Likert scale. The night shift and day shift workers of a case organization (50 each) have been measured for perception towards work stress. The value of 1 stands for strongly disagree, 2 for disagree, 3 for neutral, 4 for agree and 5 stands for strongly agree.
DOI: 10.17632/v8br9dgvsw.1
title: US Green Goods SMEs, 2008-2012

description: Measuring dynamic capabilities in new ventures: Exploring strategic change in US green goods manufacturing using website data. Firm website and Dun and Bradstreet (DUNS) measures, US Green Goods SMEs, 2008-2012 (212 data variables; 298 total observations; 223 observations without missing variables). Anonymized. Stata dta format and Stata do file. The website data for the target firms is derived from archived website data from the Wayback Machine. We also use business data for these firms from Dun and Bradstreet. See associated paper for added details including definitions of "green goods" manufacturing and small and medium-sized enterprises (SMEs); enterprise sample selection; and methods applied to use website data combined with other available business data to gauge enterprise capabilities for market sensing and responding. In the analytic model, we use data variables for two time periods, 2008-09 and 2010-11, to explain sales growth for green goods enterprises in two later time periods, from 2010 to 2012.
DOI: 10.17632/wgz2z9wm7n
title: Dataset: Initiation and internal structure of temperate coral reef community over the past 7.3 kyr in Kushimoto, mainland of Japan (Version 2)

description: The dataset is composed of the following data:
(1) A Photograph of the drilled core (WK-Ku-Gw-C3). 
(3) A description of the drilled core (WK-Ku-Gw-C3). 
(3) A list of 193 tropical cyclones that passed within a 300 km radius of the survey site from 1951 to 2023.
DOI: 10.17632/wyxjndwkbp.1
title: METAGUI 3: A graphical user interface for choosing the collective variables in molecular dynamics simulations

description: Molecular dynamics (MD) simulations allow the exploration of the phase space of biopolymers through the integration of equations of motion of their constituent atoms. The analysis of MD trajectories often relies on the choice of collective variables (CVs) along which the dynamics of the system is projected. We developed a graphical user interface (GUI) for facilitating the interactive choice of the appropriate CVs. The GUI allows: defining interactively new CVs; partitioning the configurations into microstates characterized by similar values of the CVs; calculating the free energies of the microstates for both unbiased and biased (metadynamics) simulations; clustering the microstates in kinetic basins; visualizing the free energy landscape as a function of a subset of the CVs used for the analysis. A simple mouse click allows one to quickly inspect structures corresponding to specific points in the landscape.
DOI: 10.17632/z2bmdwwdf9.2
title: Supplementary Material for: Guidelines of care for the management of acne vulgaris

description: A multidisciplinary workgroup conducted a systematic review and applied the GRADE approach to develop evidence-based recommendations related to the management of acne vulgaris in adults, adolescents, and preadolescents aged 9 years or older.
DOI: 10.17632/x95yvmj8c2.1
title: Digitalization in Mexican enterprises

description: Analysis of the degree of digitalization of SMEs in Mexico
DOI: 10.17632/xrfsd5jvzr.1
title: Synthetic HDR Image Dataset For Quality Assessment Research

description: Synthetic HDR Image Dataset

This dataset contains 300 synthetic High Dynamic Range (HDR) images generated using a Python script. Each image is 256x256 pixels in size and is created by combining different intensity layers to simulate HDR effects. The images are further processed using random gamma correction to emulate tone mapping, followed by the addition of random noise to create a realistic HDR-like appearance.

Total Images: 300
Image Size: 256x256 pixels
Format: PNG
Color Channels: 3 (Simulated RGB)
The dataset can be used for various image processing tasks, including HDR tone mapping, color correction, and machine learning experiments involving synthetic HDR content.
DOI: 10.17632/zs4w3mdtbx.5
title: LCSC VOC 2021-2022 Indoor Disinfectant Dataset

description: This dataset is of indoor air measurements before, during and after use of COVID-19 disinfectants [Lysol® Power Bathroom Cleaner (2.50% citric acid), Clorox™ Clean-Up Cleaner+Bleach Original (1.84% sodium hypochlorite),  diluted Clorox™ Regular Bleach (0.1% sodium hypochlorite), LabChem Reagent Grade Isopropanol (70% isopropanol in water), and EcoLab® Peroxide Multi Surface Cleaner and Disinfectant (8% hydrogen peroxide)]. 

Air samples were collected via sorbent tubes and analyzed through thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS). Concentration levels and thus human exposure levels of 112 volatile organic compounds (VOCs) in indoor air were assessed in educational (office and classrooms) and residential (bathrooms and kitchens) spaces.
DOI: 10.17632/vs4kmpb2st.1
title: Data for: Lower Transvaal Supergroup stromatolites (Kanye Basin, Botswana): pathway toward the formation of an oxygenated ocean
DOI: 10.17632/zn9d3pk59r.1
title: AI-Powered Plant Pathology: Unveiling Tea Leaf Diseases with a Comprehensive Dataset

description: 1.The Significance of tea leaf in Horticulture and Disease Challenges
Tea leaves are highly prized in the horticultural sector for their aesthetic worth as well as their economic relevance. However, a number of illnesses that can negatively affect the health and production of these plants frequently provide a barrier to their cultivation. To reduce these hazards, leaf disease identification must be done accurately and early. In response to this requirement, the Tea Leaf Disease Dataset was created to help researchers, horticulturists, and machine learning specialists recognize and categorize illnesses that impact tea leaves. High-resolution photos of tea leaves, showing both healthy and sick specimens, are included in this collection. The dataset contains notable illnesses including anthracnose and gray blight. Precise model training and validation are made possible by the thorough annotation of every image with information on the nature and severity of the illness. Metadata that offers crucial contextual information about the locations and weather at the time of image collection is added to the dataset, further enriching it. This contextual information improves the accuracy of prediction models and is essential for comprehending the factors driving the development of illness.
2. The Role of Deep Learning and Computer Vision 
The tea leaf Disease Dataset has a plethora of opportunities for classification and detection tasks that can be addressed by modern deep learning and computer vision techniques. 
3. A Comprehensive Dataset for Deep Learning in tea leaf Disease Classification 
This dataset was created specially to aid in the creation of sophisticated deep learning models. To ensure accuracy and relevance, the dataset's classifications were developed in conjunction with subject matter specialists from a leading agricultural institute. 
4. Data Collection and Augmentation 
At the Allynugger Duncan Tea Garden site in Moulvibazar, Sylhet, Bangladesh, a total of 3900 photos were captured. These images showed examples of healthy leaves, anthracnose, and gray blight. 1900 augmented pictures were produced from the initial photo collection in order to enhance the dataset and increase the number of data points that were accessible. Several methods, including flipping, rotating, shearing, brightness alteration, and magnification, were used to create these enhanced samples.
DOI: 10.17632/pdrxyfprfk.1
title: Rotor & bearing vibration dataset

description: Rotor and bearing vibration of a large flexible rotor (a tube roll) were measured. This dataset consists of over 5000 samples each containing 100 rounds of measured data. During the measurement, the rotating speed of the rotor was varied between 4 Hz and 18 Hz and the horizontal foundation stiffness was varied between 2.04 MN/m and 18.32 MN/m. Data sampling events were triggered with a rotary encoder 1024 times per revolution. The data in this dataset has been resampled to 2000 Hz. Rotor vibration is expressed as the center-point motion of the middle cross-section calculated from four displacement signals with a four-point error separation method. Four-point error separation method is further explained by Tiainen & Viitala (2020). Bearing vibration is expressed in terms of radial bearing forces. Measurement setup and procedure is explained by Viitala & Viitala (2020). Each 100-round sample consists of 8 time-series signals. In each 100-round sample the columns indicate same signals: - column 1 is the horizontal center-point movement in the middle cross-section of the rotor - column 2 is the vertical center-point movement in the middle cross-section of the rotor - column 3 is the horizontal force at bearing housing 1 - column 4 is the first vertical force at bearing housing 1 - column 5 is the second vertical force at bearing housing 1 - column 6 is the horizontal force at bearing housing 2 - column 7 is the first vertical force at bearing housing 2 - column 8 is the second vertical force at bearing housing 2 There are two vertical force signals for both bearing housings because two force sensors were placed under both bearing housings. The vertical resultant force can be solved by adding the vertical force signals of the corresponding bearing housing together. Each 100-round sample is in a separate file. Each file has been named with the following convention: “Nominal rotating speed_nominal horizontal support stiffness_measured rotating speed.csv”.
DOI: 10.17632/4342867j9g.1
title: Dataset of a numerical model of a cold-formed, C-shaped section in compression according to EN 1993-1-14

description: This dataset contains all data from the Data in Brief article: D. Ungermann, T. Lemański, B. Brune "Dataset of a numerical model of a cold-formed, C-shaped section in compression according to EN 1993-1-14".
It contains ANSYS input files and Excel files with the data from experimental tests and numerical simulations.
DOI: 10.17632/4dmj2n5tgy.1
title: Data for: Effects of CSR towards stakeholders and environmental management on responsible innovation and competitiveness: Evidence from North African SMEs

description: Corporate social responsibility, responsible innovation and competitiveness of North African SMEs
DOI: 10.17632/5gpp4s3gny.1
title: Data for: Caring Behavior Perceived by Nurses Patients and Nursing Students in Indonesia
DOI: 10.17632/sjvrbxrvh8.2
title: Sattriya-08 Double Handed Mudra’s Dataset

description: Sattriya_08_Mudra’s Dataset 
Contributors:
Chayanika Sarmah
Parismita Sarma
Description
There is a double handed digital image dataset of “Sattriya Dance” mostly practiced in Assam. Contributors have uploaded all the images under the title "Sattriya-08 Double Handed Mudra’s Dataset". Double handed mudra's dataset consists of eight (08) double handed mudras of Sattriya dance (Classical dance from Assam). In this repository eight sub folders are created with the names of the eight mudras whose image samples are uploaded here. Each of these eight sub folders have three sub-sub folders with titles " Front_view”, “Left_view” and “Right_view”. The sub-sub folders depict front, left and right views of all the recorded mudras. All the samples are collected by visiting a few prominent Sattriya Dance academy situated in Guwahati.  20 to 30 seconds duration videos were recorded for all the mudras at least from 50 dancers on a coloured background (afterwards background was removed). Video recording was performed using a DSLR Canon Camera (EOS 1200 D). Individual video was converted into image frames and thus were able to get around 2400 .png image frames. Some still images are also captured using the same DSLR Canon Camera (EOS 1200 D). The names and meaning of each Mudra were confirmed after a discussion with a Sattriya dance experts from Guwahati, Assam, India.
	The usage of these data sets are
1.	Developing algorithm for automatic recognition of Mudras.
2.	Developing technique for tracking and analyzing the temporal evolution of Mudras in a dance performance.
DOI: 10.17632/xfgc4zjw62.1
title: Born in Bradford’s Age of Wonder cohort: protocol for adolescent data collection - extended data

description: Additional files to support protocol paper 'Born in Bradford’s Age of Wonder cohort: protocol for adolescent data collection'
DOI: 10.15468/dl.7p6keh
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bothrops itapetiningae (Boulenger, 1907)",
 "Year 2000-2024"
 ]
}

 The dataset includes 6 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009303-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/x4gr6mmwy4.1
title: Dental Implant Dataset

description: Image datasets are crucial resources across a wide array of fields, including computer vision, machine learning, medical research, and social sciences. These datasets serve as repositories of visual data that enable researchers, developers, and professionals to train and validate their models, algorithms, and theories. Access to diverse and well-curated image datasets allows researchers to explore new possibilities, enhance the accuracy and robustness of their models, and gain valuable insights into patterns, trends, and relationships within the visual data. 
In the realm of medical and dental research, an image dataset specific to dental implants is of paramount importance. Such a dataset offers a valuable resource for researchers, dentists, and clinicians to identify, classify, and study various types of dental implants and their success rates. By analysing these images, experts can develop more accurate diagnostic algorithms and treatment planning systems, leading to prompt and effective patient care. This helps prevent complications and improves overall treatment outcomes. Additionally, a comprehensive dataset allows for the exploration of treatment patterns, anatomical factors, and potential improvement strategies. In summary, a dental implant image dataset plays a pivotal role in advancing research, improving clinical practices, and ensuring the overall health and success of dental treatments.
The specific dental implant dataset in question contains a diverse collection of 5273 anonymized and deidentified high-resolution panoramic dental X-ray images (orthopantomography or OPG) from multiple patients. These images are stored in PNG format and were collected at a dental clinic in Pune, India. The dataset is categorized into four distinct classes, each covering a different type of dental implant: Endosteal, Transosteal, Subperiosteal, and Zygomatic implants. Each category is labelled and organized in separate folders, ensuring easy access and identification of specific implant types.
Endosteal implants, the most frequent type of dental implant, are surgically inserted directly into the jawbone. These implants are made of titanium posts or screws that gradually fuse with the surrounding bone, forming a solid foundation for artificial teeth. They support various prosthetic options, such as single crowns, bridges, and implant-supported dentures.
DOI: 10.17632/772yjvvkmt.1
title: Supplementary data: Comparative energy, economic, and environmental analyses of power-to-gas systems integrating SOECs with steam-electrolysis and co-electrolysis and methanation

description: The supplementary raw data set is associated with the article titled "Comparative energy, economic, and environmental analyses of power-to-gas systems integrating SOECs with steam-electrolysis and co-electrolysis and methanation" in Data in Brief journal.
DOI: 10.17632/tvfpcdsnrb.1
title: Data from Earthquakes with Magnitude M≥4.0 from the AFAD (Disaster and Emergency Management Presidency) Disaster Period Earthquake Catalogue System

description: This Data from earthquakes with magnitude M≥4.0 from the AFAD (Disaster and Emergency Management Presidency) Disaster Period Earthquake Catalogue system were used. (Link to the source from which the data was taken: https://deprem.afad.gov.tr/event-instrumental). This data set includes parameters such as latitude, longitude, depth, date of the earthquake, earthquake magnitude and location of the earthquake.
DOI: 10.17632/nmmz4wjvxk.1
title: Supplements to the article: Distinct gene-set enrichment patterns underlie common generalized and focal epilepsies

description: Supplemental data to the article: Distinct gene-set enrichment patterns underlie common generalized and focal epilepsies.
DOI: 10.17632/2wg6m397dt.1
title: Replication Package for “Do the Rich Gamble in the Stock Market? Low Risk Anomalies and Wealthy Households”

description: Replication Package for the paper “Do the Rich Gamble in the Stock Market? Low Risk Anomalies and Wealthy Households” by Turan G. Bali, A. Doruk Gunaydin, Thomas Jansson, and Yigitcan Karabulut. 

Abstract of the paper:

Contrary to the theoretical principle that higher risk is compensated with higher expected return, the literature shows that low-risk stocks outperform high-risk stocks. Using a large-scale household dataset, we provide an explanation for this puzzling result that the anomalous negative risk-return relation is only confined to those stocks predominantly held by rich households, whereas the anomaly disappears for stocks held by non-rich households and institutional investors. We find that social status concern of rich households and the induced lottery preference explain wealthy investors’ demand for high-risk stocks, leading to overpricing and low future returns for such stocks.

The full paper can be found at https://papers.ssrn.com/sol3/Papers.cfm?abstract_id=3664501.
DOI: 10.17632/jw8ydrxg4m.1
title: Distribusi Data pada Perpustakaan Digital

description: Distribusi data tersebut dapat dilihat bahwa pemustaka dalam komunitasperpustakaan digital, dapat memberikan koleksi digitalnya kepada perpustakaan. Pada bagianpengolahan koleksi tersebut diseleksi terlebih dahulu kemudian jika memenuhi syarat dantidak ada duplikasi pada koleksi yang dimilikinya maka baru dibuatkan metadata
DOI: 10.17632/89bhydjkzv.1
title: Base images for 360 degrees panorama with camera poses

description: This dataset presents base images for 360 degrees image construction through stitching. The HD resolution base images are already undistorted. This dataset is meant for 360 degrees panorama generation and optimization techniques comparison. Each dataset folder presents a "cameras.sfm" file that contains the camera pose for every acquired image, which is estimated from IMU and servo motor sensors. The file presents the total number of images in the first line. Considering the rotation matrix as 'R' (with dimensions 3x3) , translation vector as 't' (with dimensions 3x1), 'fx' and 'fy' as the focal lengths in both X and Y axis, and 'cx' and 'cy' as the principal point coordinates in the image, every following line is written as: "image_name.png" R11 R12 R13 R21 R22 R23 R31 R32 R33 t11 t21 t31 fx fy cx cy with the numbers representing the position in the matrix and vector. In this way, every image has an approximated first guess for it's pose.
DOI: 10.17632/9c4brbnms3.1
title: Dataset on Consumers' KAP: Investigating Electric Vehicle Adoption in the Indian Automobile Sector.

description: This dataset provides a comprehensive exploration of consumers' perspectives on electric vehicle (EV) adoption in the Indian automobile sector, capturing crucial insights into their knowledge, attitudes, and practices. It encompasses diverse variables, including awareness levels, perceptions, and behavioral patterns, offering a nuanced understanding of the factors influencing the decision-making process regarding EV adoption. With a focus on the Indian market, this dataset serves as a valuable resource for researchers and industry stakeholders seeking to drive sustainable and informed strategies for the future of electric mobility in the region.