cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/ntywtmc2p7.1
title: Data for: The Influence of Personality Traits and Order of Play on Performance: A Basketball Free Throw Field Experiment

description: Data of a basketball free throw field experiment
DOI: 10.17632/2jc8924xnr.1
title: Sustainability innovation textile review

description: Sustainability innovation textile review
DOI: 10.17632/3jgf74nsgd.1
title: Database of endemic and/or threatened Orthoptera species in the Natura 2000 sites of Greece.

description: Scientific context Greece hosts 378 Orthoptera species (35% of Europe), with 140 species to be endemic in Greece and 139 species to be endangered after IUCN criteria. One fourth of the Greek orthoptera fauna is endemic and endangered. The Greek biodiversity law 3937/31-3-2011 provides protection to all the above species as “species included in the national and international red-lists, or “endemic species” (Article 11: conservation of endemic biodiversity) Description The database presents the occurrence of 145 Orthoptera species in 186 terrestrial Natura 2000 sites of Greece. The selected species presented are: (a) threatened according to IUCN criteria European level: Critically Endangered-CR, Endangered-EN, Vulnerable-VU and Near Threatened-N, (b) endemic in Greece or (c) both threatened and endemic in Greece. The conservation status of the species is provided by the European Red List of Grasshoppers, Crickets, and Bush-crickets [1]. The species occurrence data are provided by the book “The Grasshoppers of Greece” [2] plus unpublished data from the contributors (Willemse, L., Kati, V. 2019, 2020). The database includes 1553 records of known localities of species occurrence. Significance, use, limitations The database can be used: As a reference database for Environmental Impact Assessment Studies and Special Ecological Studies in Natura 2000 sites. To promote grasshopper research, conservation, monitoring, and encourage citizen-science in Natura 2000 network. The database needs regular updating. While the species occurrence in Natura 2000 sites is considered reliable, the background map was of medium confidence: species occurrences were mapped after exact GPS coordinates or locality description (less accurate). References [1]. Hochkirch, A., et al. 2016. European Red List of Grasshoppers, Crickets, and Bush-crickets. Luxembourg: Publications Office of the European Union. 94 p. Available at: https://portals.iucn.org/library/sites/library/files/documents/RL-4-021.pdf . [2]. Willemse, L.P.M., Kleukers, R.M.J.C, Ode, B. (2018): The Grasshoppers of Greece. EIS Kenniscentrum Insecten & Naturalis Biodiversity Center, Leiden. 440p.
DOI: 10.17632/pnvv34cm37.2
title: Golf-related engagement during COVID-19 quarantine restrictions, 4-12th May 2020

description: This Data Report presents a description of a research dataset on golf-related engagement amongst golfers during a period of quarantine as a result of COVID-19 pandemic.
DOI: 10.17632/85jwj5gcdc.1
title: Data Halocarbon GHG 48

description: Repository
1.  Raw Gaussian output files (.out) obtained for 48 halocarbon molecules (GHG) for equilibrium structure, vibrational frequencies, rotational constants, Mulliken charges, and other detailed information for all compounds at B3LYP/6-311++G(3df,3pd), M06-2X/6-311++G(3df,3pd), MN15/6-311++G(3df,3pd), PBE0/6-311++G(3df,3pd) and TPSS/6-311++G(3df,3pd) levels of theory. 

2. Data sheets on excel (.xlsx) with the complete information of the 48 halocarbon molecules (GHG) with the equilibrium geometries in cartesian coordinate, vibrational frequencies and thermochemical information.
DOI: 10.17632/mgj29ndgrk.5
title: AHD: Arabic Healthcare Dataset

description: - Numerous language-centric research on healthcare is conducted day by day. To address shortcomings of Arabic natural language generation models, we introduce a large Arabic Healthcare Dataset (AHD) of textual data. For this motivation, we named our dataset ‘AHD’.
- The largest Arabic Healthcare Dataset (AHD) as we know was collected from altibbi website. 

- The AHD consists of more than 808k Question and Answer into 90 variety categories. The AHD contains one file, and the file description will be discussed here. One file is the actual data which is in Arabic language. 

 - AHD.xlsx file contains dataset in excel format, which includes the question, answer, and category in Arabic.

 - AHD_english.xlsx file contains dataset in excel format, which includes the question, answer, and category translated to English.

- Distribution of Question and Answer per category.xlsex shows the distribution of the data set by category.
DOI: 10.17632/hvm4wh3jzx.1
title: Turning Dataset for Chatter Diagnosis Using Machine Learning

description: This dataset corresponds to sensor signals from several cutting tests including two perpendicular single axis accelerometers, a tri-axial accelerometer, a microphone, and a laser tachometer. We post both the raw data from all the sensors as well as the conditioned and tagged data from one channel of the tri-axial accelerometer. The tags that we use are: no-chatter, intermediate chatter, chatter, and unknown. The cutting test is performed by turning an Aluminum 6061 workpiece on a Clasuing-Gamet 33 cm (13 inch) engine lathe using a 0.04 cm (0.015 inch) radius Titanium nitride coated insert attached to an S10R-SCLCR3S boring bar. We provide a picture of the experimental setup in the included documents. Data from four different cutting configurations were collected where each cutting configuration depends on the stickout distance, which is the distance from the heel of the boring rod to the back face of the tool holder. Four stickout distances were considered: 5.08 cm (2 inch), 6.35 cm (2.5inch), 8.89 (3.5inch), and 11.43 cm (4.5 inch). For each stickout distance, we collect data for several combinations of the rotational speed and depth of cut. We include a table showing the parameter combinations and the number of the tagged time series corresponding to each stickout length in the included documents. We also briefly describe the algorithm we used to tag the data.
DOI: 10.17632/zhtvkny53n.1
title: Medicinal Products in Bangladesh - A Dataset of Generic and Brand Names, Dosage Strengths, and Manufacturers

description: This dataset provides detailed information on medicinal products available in Bangladesh, including generic names, brand names, dosage strengths, and manufacturers. It is designed primarily to fine-tune open-source large language models, such as LLAMA, GPT-Neo, BLOOM, and others, with country-specific pharmaceutical data. By utilizing this dataset, researchers and developers can enhance models' capabilities to provide accurate, localized insights into the pharmaceutical sector in Bangladesh. The dataset is also suitable for healthcare policy research and analysis of drug manufacturing trends in the region, making it a valuable resource for both AI development and pharmaceutical studies.
DOI: 10.17632/hps7rcbwm6.1
title: Data Repository

description: We have used the dataset of Joint EVS/WVS 2017-2020, allowing for comparisons of tax morality in more than 79 countries worldwide, applying machine learning variable selection methods suited to categorical variables (Chi-Squared and mutual information).
The empirical results revealed that variables like: religious denomination, faith in God and the importance of God, together with the level of trust in people from other religion, confidence in churches have been connected with a significantly increased the extent of tax morale, being the most relevant variables of religiosity.
DOI: 10.17632/xcxspv8c2r.1
title: Physical layer data acquisition of IRIDIUM satellites broadcast messages

description: This dataset refers to a measurement campaign carried out in two periods, i.e., from Aug. 12th to Sept. 10th, 2020 and Sept. 11th to Oct. 9th, 2020. The dataset includes a total of 2060299 entries (IRIDIUM broadcast messages), and the associated 7,628,604,575 IQ samples.
DOI: 10.17632/dtsn3xn3n6.1
title: Global chronostratigraphical correlation table for the last 2.7 million years v.2019 (Poster version)

description: This is a poster version of Figure 1 of the Quaternary International 500 article Cohen, K.M. & Gibbard, P.L. 2019 Global chronostratigraphical correlation table for the last 2.7 million years. Quaternary International vol. 500. Fig. 1. https://doi.org/10.1016/j.quaint.2019.03.009 See abstract and full text of the linked paper for details, research history and citations/references to materials included in the chart: it makes extensive use of published data sets and schemes, all cited in the main text, many of which have data repositories. The 2019 QI-500 paper publication follows up the Episodes 2008 Gibbard & Cohen paper. The 2019 edition of the chart (QI-500/INQUA-Dublin), follows up the 2016 IUGS' Cape town IGC version (web-released). The Figure 2 inset is also included here, for completeness. The release of the poster version via data.mendeley.com, cross-referenced from the QI paper, was coordinated with the QI journal management (Elsevier) and editorship (INQUA). Use of the INQUA Dublin logo was coordinated with the congress organisation. Use of the IUGS-ICS and INQUA logo's was coordinated with the respective organisations (and is continued from earlier releases of the chart). The undivided poster version only differs from the 3-page spread figure in the QI paper in: placement and fontsize of title and version, placement of logotypes, inclusion of Utrecht University and University of Cambridge logo's on the poster (continued from earlier releases of the chart). The PDF files contain scale-able vector graphics only and therefore should suitreproductions to mini-poster (A4, A3) and poster size (e.g. A1) formats alike. Before seeking far zoomed in usage of the chart: take notice of the discussion in the QI paper of chosen graphical resolution, versus achievable geological and chronometric correlation accuracy limits.
DOI: 10.17632/4kgpfmgshb.1
title: Interactive effects of trait and state anxieties on time perception

description: The data of the paper "interactive effects of trait and state anxieties on time perception" including the raw data, the analyzed data, and the materials of the experiment
DOI: 10.17632/rb9y2vbwcb.1
title: Data for: Middle-late Miocene rapid exhumation of the southern Qilian Shan and implications for propagation of the Tibetan Plateau

description: Detailed (U-Th)/He Age Data
DOI: 10.17632/yxr5gtgwjt.1
title: A Footworm in the door: Revising Onchocerca phylogeny with previously unknown cryptic species in wild North American ungulates

description: Onchocerca is an important genus of vector-borne filarial nematodes that infect both humans and animals worldwide. Many Onchocerca species, most of medical and veterinary health relevance, are the focus of a variety of diagnostic and molecular research. However, despite the importance of these parasites, there is growing evidence of previously unexplored genetic diversity of these nematodes, particularly among wild ungulate hosts in North America. These understudied parasites prevent us from comprehending the evolutionary history of the genus Onchocerca, monitoring potential One Health threats, and improving our filarioid diagnostic capabilities. In order to fill these knowledge gaps, we identified five uncharacterized Onchocerca lineages and compared them to other well-known filarioid species using single and concatenated gene regions (i.e., nd5, cox1, 12S, 18S, 28S, hsp70, MyoHC, rbp1). Phylogenetic analyses revealed that the novel Onchocerca lineages of wild North American ungulates segregate into two clades. One clade comprised Onchocerca lineages II, IV, and V and other species found mainly in domestic animals and humans, and the second comprised Onchocerca lineages I and III and other species from a variety of hosts including cervids, bovids, and equids. The formation of two clearly separate clades supports the idea of at least two independent expansion events of ancestral Onchocerca species into the North American continent via the Bering land bridge. Cophylogenetic analysis shows evidence of ancestral Onchocerca species of Bovidae host-switching to wild Cervidae and giving rise to the novel Onchocerca species. Lastly, pairwise analysis confirms informative molecular markers of diagnostic relevance in both mitochondrial and nuclear gene regions of filarioid nematodes. The overall information provides greater context to the genus Onchocerca and emphasizes the need to discover, characterize, and monitor neglected parasites, especially those of wildlife origin.
DOI: 10.15468/dl.9bg96z
title: Occurrence Download

description: A dataset containing 3203 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Prunus domestica L.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 3203 records from 220 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009315-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/gwf2rtc9z2.1
title: Good and Bad classification of Holy Basil Leaves (Ocimum sanctum)

description: data description within 3000 character for project Good and Bad classification of Holy Basil Leaves (Ocimum sanctum)) which contain 500 good and 500 bad sample of Marsh Barbel Leaves
ChatGPT
The project titled "Good and Bad Classification of Holy Basil Leaves (Ocimum sanctum)" focuses on creating a robust system for differentiating between good and bad samples of Holy Basil leaves using a dataset consisting of 1,000 samples. Holy Basil, known scientifically as Ocimum sanctum, is a plant widely revered for its medicinal properties and is integral to various traditional health practices. The quality of these leaves is crucial for their efficacy in medicinal use, making this classification task both practically significant and scientifically valuable.

Data Description
Dataset Composition:

Total Samples: 1,000
Good Samples: 500
Bad Samples: 500
Features:
The dataset contains various features that are crucial for the classification task. These features are derived from both visual inspection and possibly from chemical analysis. Here’s a breakdown of potential features:

Visual Features:

Color: RGB or HSV values capturing the color distribution of the leaf.
Texture: Measures such as smoothness, roughness, or the presence of visible spots.
Shape: Geometric descriptors including length, width, and the perimeter of the leaves.
Vein Patterns: Analysis of the venation patterns which may indicate health.
Defects: Presence of holes, discolorations, or other deformities.
Chemical Features:

Moisture Content: Levels of water within the leaf, indicating freshness.
Nutrient Levels: Concentrations of essential oils, vitamins, and minerals.
pH Levels: Acidity or alkalinity which might affect the leaf's condition.
Labeling:

Good Samples: These are leaves that are fresh, free from significant defects, and contain optimal levels of beneficial compounds.
Bad Samples: These leaves may show signs of aging, disease, pest infestation, or have suboptimal levels of essential nutrients.
Data Collection:

Visual Data: High-resolution images taken under controlled lighting conditions to ensure consistency in color and texture representation.
Chemical Data: Laboratory tests conducted to measure the chemical properties of the leaves accurately.
Data Preprocessing:
To prepare the data for analysis and model training, several preprocessing steps are employed:

Image Augmentation: Techniques such as rotation, scaling, and flipping to increase the variability of the visual dataset.
Normalization: Standardizing the values of chemical features to ensure they are on a comparable scale.
Feature Extraction: Applying methods such as Principal Component Analysis (PCA) to reduce dimensionality while retaining significant information.
Classification Methodology:
The goal of the project is to develop a machine learning model capable of accurately classifying the leaves. Potential approaches include:
DOI: 10.17632/bpnmf2vhsk.1
title: Serial 3D Dental Models

description: The dataset consists of 3D scans of the upper and lower dentitions of 24 patients, all in their growth age, taken at different time intervals. All patients had normal occlusions with no history of orthodontic treatment. There are 3 scans avaialble for 8 out of 24 patients, and 2 scans available for the remaining 16 patients. The interval between each two consecutive scans is at least 2 years and at most 10 years. The dataset was created by 3D scanning of plaster models using a benchtop structured-light 3D scanner (Maestro 3D Desktop Scanner, Pontedera, Italy) with a point accuracy of &lt;10 μm and resolution of &lt;70 μm in all directions.
DOI: 10.17632/szkr2wjbnk.1
title: Faulting prediction models

description: Dataset and codes used for faulting prediction models
DOI: 10.17632/rc39hksfvz.1
title: Cotton leaves Dataset for diseases classification using EfficienNet Model

description: The Cotton Disease Classification Dataset for the efficienNet Model is prepared for the agricultural research analysis. The dataset is based on images captured in the fields of cotton crops. this dataset helps to find disease types on the cotton crop leaves. the main purpose is to diagnose disease types on cotton crop leaves.
DOI: 10.17632/9t2s2vv343.1
title: H2 photo-absorption cross section dataset

description: This dataset contains the high-resolution H2 photo-absorption cross section of that is used in Chadney et al. (2021). The cross-section is composed from the following sources:
  - 0.1 - 80.4 nm: Backx et al. (1976) measurements,
  - 80.4 - 84.6 nm: Dalgarno and Allison (1969) + Yelle et al. (1993) calculations at 250K,
  - 84.6 - 120 nm: Yelle et al. (1993) calculations at 250K.

For more information on this dataset, see Chadney et al. (2021).

References:
Backx, C., Wight, G.R., der Wiel, M.J.V., 1976. Oscillator strengths (10-70 eV) for absorption, ionization and dissociation in H2, HD and D2, obtained by an electron-ion coincidence method. Journal of Physics B: Atomic and Molecular Physics 9, 315–331.

Chadney, J.M., Koskinen, T. T., Hu, X., Galand, M., Lavvas, P., Unruh, Y. C., Serigano, J., Hörst, S. M, Yelle, R. V., 2021, Energy deposition in Saturn’s equatorial upper atmosphere, Icarus, accepted.

Dalgarno, A., Allison, A.C., 1969. Photodissociation of molecular hydrogen on Venus. Journal of Geophysical Research 74, 4178–4180.

Yelle, R., Herbert, F., Sandel, B., Vervack, R., Wentzel, T., 1993. The Distribution Hydrocarbons in Neptune’s Upper Atmosphere. Icarus 104, 38–59.
DOI: 10.17632/52bmtrhsct.2
title: Project 4 - Technology for Elderly (UX Journey with Generative AI)

description: A UX journey case study involves a comprehensive analysis of the user experience for a specific product or service. The case study would likely include a description of the problem or opportunity that the product/service is intended to address, the target user group, and the context in which the product/service is used.

The case study would also include a detailed description of the design process, including user research, prototyping, testing, and iteration. This would involve identifying user needs and preferences, creating wireframes and mockups, and conducting user testing to refine the design. 

This data is a dataset from a pilot experiment to analyze the productivity of novice developers. In Project 4 with the topic Technology for Elderly, developers are asked to do an analysis using UX Journey and AI Generative. The structure of this data set consists of:
1. Original file: Initial analysis file from novice developer.
2. Revision 1: The result of the first improvement of several substantial notes.
3. Revision 2: The result of the second improvement from several substantial notes.
4. Revision 3: The result of the third improvement from several substantial notes.
5. Evaluation Rubric: Tests conducted by novice developers, through self-reviews, peer reviews, and walkthrough evaluations.
6. Self Efficacy: Results of the General Self Efficacy Scale questionnaire
7. Time and User Stories: Recap the time the respondents need to work on and the results of the user stories collected.
DOI: 10.17632/mgcf8h9hyt.1
title: Brazil_Power_Dist_Reliaibility_SEGAN

description: Source files and python notebook code for the paper "PROBABILISTIC SIZING OF LOW VOLTAGE MICROGRID-FORMING RESOURCES: TECHNICAL APPROACH AND REGULATORY ASSESSMENT IN THE BRAZILIAN CONTEXT"
DOI: 10.17632/78mw556vjc.2
title: Fall Risk Assessment Dataset: Older-Adult Participants undergoing the Time Up and Go Test

description: The dataset comprises signal data collected from IMU sensors during the administration of the Time Up and Go (TUG) test for assessing fall risk in older adults. The dataset is divided into two main sections. The first section contains personal, behavioral, and health-related data from 34 participants. The second section contains signal data from tri-axial acceleration and tri-axial gyroscope sensors embedded in an IMU sensor, which was affixed to the participants' waist area to capture signal data while they walked. The chosen assessment method for fall risk analysis is the TUG test, requiring participants to walk a 3-meter distance back and forth. To prepare the dataset for subsequent analysis, the raw signal data underwent processing to extract only the walking periods during the TUG test. Additionally, a low-pass filter technique was employed to reduce noise interference. This dataset holds the potential for the development of effective models for fall risk detection based on insights garnered from questionnaires administered to specialists who observed the experiments. The dataset also contains deanonymized participant information that can be explored to investigate fall risk, along with other health-related conditions or behaviors that could influence the risk of falling. This information is invaluable for devising tailored treatment or rehabilitation plans for individual older adults.
DOI: 10.17632/tbnj7rbft7.1
title: Code for: Investor sentiment and advertising expenditure

description: There are two files uploaded. First file, 'Creating variables.sas' contains SAS code used to put the data together and the second file, 'Regressions adv_sent.do' contains STATA code used to run regression models.
DOI: 10.17632/4kdvvkfpdk.1
title: Clascoterone in the Treatment of Mild Hidradenitis Suppurativa -  Supplemental Material 

description: Supplemental Figure 1. Hidradenitis suppurativa before and after Clascoterone therapy. On top left, bilateral thighs before treatment; on top right, bilateral thighs after treatment, note absence of new nodules in a patient who previously developed nodules constantly; on bottom left, bilateral thighs before treatment; on bottom right, bilateral thighs after treatment, note nodule reduction. The patient whose images are on the top left and top right did not have any concomitant therapy for HS. The patient whose images are on the bottom left and bottom right was prescribed topical clindamycin concurrently, however she reported using only clascoterone. 

Supplemental Table 1. Individual Baseline Demographics and Clinical Outcomes of Patients with Mild Hidradenitis Suppurativa Treated with Topical Clascoterone 1% Cream and Summarized Demographics of Excluded Patients
DOI: 10.17632/xkbhxc53wc.1
title: Sepsis Simulation

description: Figure of simulation result
DOI: 10.17632/trghs22fpg.1
title: A Composite Retinal Fundus and OCT Dataset along with Detailed Clinical Markings for Extracting Retinal Layers, Retinal Lesions and Screening Macular and Glaucomatous Disorders

description: This repository contains composite retinal fundus and OCT dataset for analyzing retinal layers, retinal lesions, and to diagnose normal and abnormal retinal diseases like Macular Edema, Central Serous Retinopathy, Age-related Macular Degeneration, and Glaucoma.