cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/8fztxggjnc.1
title: Breast Mammography Image Dataset with Masses

description: The mammography dataset includes both benign and malignant tumors. In order to create the pictures for this dataset, 106 masses from the INbreast dataset, 53 masses from the MIAS dataset, and 2188 masses from the DDSM dataset were initially extracted. Then, we preprocess our photos using contrast-limited adaptive histogram equalization and data augmentation. Inbreast dataset has 7632 photos, MIAS dataset has 3816 images, and DDSM dataset includes 13128 images after data augmentation. Additionally, we combine DDSM, MIAS, and INbreast. The size of each image was changed to 227*227 pixels.
DOI: 10.17632/48mn4hmprt.1
title: Data for: The Relationship between the Chloride Corrosion Concentration Limit and the Interfacial Transition Zone Around Embedded Rebar

description: This database include all the data of mix composition, mix properties, materials properties, chloride threshold, image analysis results, and queries which makes the data useful.
DOI: 10.17632/hb74ynkjcn.5
title: A Database of Leaf Images: Practice towards Plant Conservation with Plant Pathology

description: The relationship between the plants and the environment is multitudinous and complex. They help in nourishing the atmosphere with diverse elements. Plants are also a substantial element in regulating carbon emission and climate change. But in the past, we have destroyed them without hesitation. For the reason that not only we have lost a number of species located in them, but also a severe result has also been encountered in the form of climate change. However, if we choose to give them time and space, plants have an astonishing ability to recover and re-cloth the earth with varied plant and species that we have, so recently, stormed. Therefore, a contribution has been made in this work towards the study of plant leaf for their identification, detection, disease diagnosis, etc. Twelve economically and environmentally beneficial plants named as Mango, Arjun, Alstonia Scholaris, Guava, Bael, Jamun, Jatropha, Pongamia Pinnata, Basil, Pomegranate, Lemon, and Chinar have been selected for this purpose. Leaf images of these plants in healthy and diseased condition have been acquired and alienated among two separate modules. 

Principally, the complete set of images have been classified among two classes i.e. healthy and diseased. First, the acquired images are classified and labeled conferring to the plants. The plants were named ranging from P0 to P11. Then the entire dataset has been divided among 22 subject categories ranging from 0000 to 0022. The classes labeled with 0000 to 0011 were marked as a healthy class and ranging from 0012 to 0022 were labeled diseased class. We have collected about 4503 images of which contains 2278 images of healthy leaf and 2225 images of the diseased leaf. All the leaf images were collected from the Shri Mata Vaishno Devi University, Katra. This process has been carried out form the month of March to May in the year 2019. The images are captured in a closed environment. This acquisition process was completely wi-fi enabled. All the images are captured using a Nikon D5300 camera inbuilt with performance timing for shooting JPEG in single shot mode (seconds/frame, max resolution) = 0.58 and for RAW+JPEG = 0.63. The images were in .jpg format captured with 18-55mm lens with sRGB color representation, 24-bit depth, 2 resolution unit, 1000-ISO, and no flash. 

Further, we hope that this study can be beneficial for researchers and academicians in developing methods for plant identification, plant classification, plant growth monitoring, leave disease diagnosis, etc. Finally, the anticipated impression is towards a better understanding of the plants to be planted and their suitable management.
DOI: 10.17632/jgpsf32f86.1
title: Supplemental Materials for JAAD Publication JAAD-D-21-01712R3, Phase 2 Randomized, Dose-Ranging Trial of CTP-543, a Selective Janus Kinase Inhibitor, in Moderate to Severe Alopecia Areata

description: These are supplemental data sets to the JAAD publication entitled "Phase 2 Randomized, Dose-Ranging Trial of CTP-543, a Selective Janus Kinase Inhibitor, in Moderate to Severe Alopecia Areata". Supplemental figures include: Figure S1: Enrollment and Patient Disposition; Figure S2: Patient SALT Improvement Thresholds; Figure S3: Percentage of Patients Achieving SALT score ≤20 Over Treatment Period; Figure S4. Patient-Reported Outcomes.
DOI: 10.17632/cdx7kbw9t5.1
title: data set on work location choice in Vietnam

description: The research questionnaire was designed by adaptation measures from previous researchs for Vietnamese context. We conducted the data collection by using Google docs. We upload soft electronic copies of survey questionnaire online. The questionnaires were sent to about 1902 email addresses, which were collected from student alumni of 5 universities in Hanoi –the capital of Vietnam. We received 510 responses (response rate of 26.8%). After screening the questionnaires, bias answers were eliminated. The final sample size consists of 502 responses.
DOI: 10.17632/7y76bwpbxx.4
title: AUTORECYCLER: AUTOMATIC WASTE CLASSIFICATION SYSTEM (PLASTIC, GLASS, CARDBOARD AND METAL) THROUGH ARTIFICIAL VISION.

description: Environmental protection has gained greater importance over time due to the negative impact and irreversible consequences that have occurred worldwide and stem from pollution. One of the great challenges faced in different parts of the world is the inadequate management and classification of solid waste. In order to contribute to tackling this issue, this paper proposes an automated sorting system based on artificial vision which allows recognition and separation of recyclable materials (Plastic, Glass, Cardboard and Metal) through a webcam connected in real time to the Nvidia® Jetson Nano™ 2GB programming board, which has a convolutional neural network (CNN) trained for the proper classification of waste. The system had a 95% accuracy in separating plastic, 96% in glass and metal, and 94% in cardboard. With this in mind, we conclude it contributes to the recycling effort, which has an impact on the reduction of environmental pollution worldwide.
DOI: 10.17632/959dxmgj8d.1
title: Classification of Heart Failure Using Machine Learning: A Comparative Study

description: Our research demonstrates that machine learning algorithms can effectively predict heart failure, highlighting high-accuracy models that improve detection and treatment. The Kaggle “Heart Failure” dataset, with 918 instances and 12 key features, was preprocessed to remove outliers and features a distribution of cases with and without heart disease (508 and 410).
Five models were evaluated: the random forest achieved the highest accuracy (92%) and was consolidated as the most effective at classifying cases. Logistic regression and multilayer perceptron were also quite accurate (89%), while decision tree and k-nearest neighbors performed less well, showing that k-neighbors is less suitable for this data.
F1 scores confirmed the random forest as the optimal one, benefiting from preprocessing and hyperparameter tuning. The data analysis revealed that age, blood pressure and cholesterol correlate with disease risk, suggesting that these models may help prioritize patients at risk and improve their preventive management. The research underscores the potential of these models in clinical practice to improve diagnostic accuracy and reduce costs, supporting informed medical decisions and improving health outcomes.
DOI: 10.17632/c7s3xny5jx.1
title: Replication files for "A Theory of Capital Flow Retrenchment"

description: These are the replication files for Davis and van Wincoop, "A Theory of Capital Flow Retrenchment" in the Journal of International Economics
DOI: 10.17632/y5jwdddw5r.1
title: Coronavirus Outbreak COVID-19

description: The data were collected based on the latest update on March 27, 2020. Data were employed to calculate the probabilities of death, recovery in each country, development of K-means clustering models and correlation among attributes.
DOI: 10.17632/5wkxzmzwnz.2
title: Experimental Data set for concrete compressive strength prediction from IIT Bhubaneswar concrete laboratory

description: The database is gathered by experimental testing concrete cube specimens, in the concrete technology laboratory of the Indian Institute of Technology Bhubaneswar, for 188 different concrete mixes. Several concrete mix proportions are tested for predicting the compressive strength of recycled aggregates concrete (RCA). For most of the mixes, coarse recycled aggregates were used as the only coarse aggregate for concrete mix testing. It includes a variation of water/binder in ranges of 0.25 – 0.75; addition of fly ash, Ground Granulated Blast-furnace Slag (GGBS), and metakaolin (MK) to the concrete mix.
DOI: 10.17632/7j94gwthv7.1
title: Source data "Superior metabolic improvement of polycystic ovary syndrome traits after GLP1-based multi-agonist therapy"

description: Dataset corresponding to the article published in the journal nature communications entitled Superior metabolic improvement of polycystic ovary syndrome traits after GLP1-based multi-agonist therapy
DOI: 10.17632/jrss9jdjz9.1
title: Synthetic oversampling for credit card default prediction

description: This dataset contains more than 17000 data of credit card holder with 20 predictor variables and 1 binary target variable. The corresponding R code for comparing several proposed (density-based) and existing synthetic oversampling methods (SMOTE-based) is also provided.
DOI: 10.17632/gptf595dwf.1
title: China MDBs Africa

description: Do files, data files and variable descriptions
DOI: 10.17632/3j9yh8znj4.3
title: Bike&Safe Dataset

description: This dataset is part of a paper:

Blauth da Silva, Germano; Tavares, João (2022). Bike&Safe: A model to support cyclist accident prevention in smart cities.

Abstract: 

A model to support cyclist accident prevention in smart cities. 

This dataset collects data from the accelerometer, gyroscope, magnetometer, and GPS sensors of the smartphone positioned on the handlebar of the bicycle.

The coordinate system is defined relative to the screen of the phone in its default orientation. The axes are not swapped when the device's screen orientation changes.
The X axis is horizontal and points to the right, the Y axis is vertical and points up and the Z axis points towards the outside of the front face of the screen. In this system, coordinates behind the screen have negative Z values.

Dataset Information:

1. The dataset was recorded in three different routes. Each route goes from point A to point B, via roads.
2. Each scenario was recorded 3 times
3. The routes are informed below:

First route: https://bit.ly/3NsJ3vx

Second route: https://bit.ly/3DR3jUk

Third route: https://bit.ly/3TXFlMU

Number of samples: 12
Number of Instances: 811781
Data Set Characteristics:  Sequential Data, Time Series Data, Multivariate Data, Correlation
Area: Traffic Safety 
Attribute Characteristics: Integer, Double
Number of Attributes: 5

Relevant Information:

1. The dataset collects data from an Android smartphone which is positioned on the handlebar of the bicycle.
2. The data sensors collected are accelerometer, gyroscope, magnetometer, and GPS.
3. The sampling frequency of the sensors is DELAY_FASTEST - 20 milliseconds for accelerometer, gyroscope and magnetometer.
4. Each recording generated four files:
     i. Accelerometer.
     ii. Gyroscope.
     iii. Magnetometer.
     iv. GPS.
5. The three first files contain the following information:
     Timestamp, Id, x axis, y axis, z axis.
6. GPS file contains the following information:
     Timestamp, Id, Latitude, Longitude, Sensor speed.
7. Data format: CSV
DOI: 10.17632/brn4xm47jk.3
title: Water consumption of crop on watershed level (blue and green water, uncertainty, incl. shapefile) and monthly irrigation water consumption

description: The data contains production and water consumption data fro 160 crops in &gt;12'000 watershed units and global coverage. Unique features: - uncertainty of irrigation water consumption in estimates - watershed level (allows quantification of water scarcity footprint) - watershed split by country to allow country footprint assessment The data contains: - a shape file "WS_ctry.zip" - a XLS file (can be joined to shapefile by field "FID"): "watershed_WF_crops.xlsx" - a XLS file (can be joined to shapefile by field "FID"): "BlueWGeoMean_Watershed_monthlys.xlsx" The Excel file "watershed_WF_crops.xlsx" contains different tabs, which can be linked to the shapefile: - total BlueWater (total m3 irrigation water consumption in watershed) - total green water (total m3 green water consumption in watershed) - Production_ton (total production of crop in watershed in metric tons) - BW_per ton (m3/ton irrigation water consumption: expected result) - BW_min_per ton (m3/ton irrigation water consumption: minimum estimate) - BW_max_per ton (m3/ton irrigation water consumption: upper estimate) - Green water per ton (m3/ton green water; natural water supply) ------ The FID can be linked to BAS34S_ID based on the "BlueWGeoMean_Watershed_monthly.xlsx" The Excel file "BlueWGeoMean_Watershed_monthly.xlsx" contains tabs of monthly BW consumption for applying temporally and spatially explicit water scarcity assessments of cropproduction. This data is also on watershed level and can be linked to the shapefile. the data is described in the "readme" tab. Details are described in Pfister and Bayer 2014 (doi:10.1016/j.jclepro.2013.11.031)
DOI: 10.17632/h4gb998bs8.2
title: Replication Pack for "Monetary Policy and Fragility in Corporate Bond Mutual Funds"

description: Replication files and pseudo data for Kuong, O'Donovan, and Zhang, 2024, "Monetary Policy and Fragility in Corporate Bond Mutual Funds" , Journal of Financial Economics
DOI: 10.17632/xzyng774cy.1
title: BOLD MRI Data

description: BOLD MRI T2* data files and MATLAB script for post-image analysis to identify regions of hypoxia and potential normoxia within a murine GBM model
DOI: 10.15468/dl.cy67c2
title: Occurrence Download

description: A dataset containing 36668265 species occurrences available in GBIF matching the query:
{
 "or" : [
 "PublishingCountry is Colombia",
 {
 "and" : [
 "Country is Colombia",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 }
 ]
}

 The dataset includes 36668265 records from 4480 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009172-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/icpsr03625.v2
title: Omnibus Study, Fall 1973
title: Version 2

description: This study was specifically designed for investigators who
wanted to collect data on a national sample, but only required part of
an interview. The survey explored the following topics: economic
situation, attitudes toward the political system, views and beliefs
about selected institutions, philanthropic contributions, and
background questions. In the economic section respondents were asked
how they viewed their own and the country's economic situations in
comparison to the previous year, and what their expectations were for
the next 12 months. Questions referred to personal finances,
employment, price changes, and the national business situation. The
economic variables are also included in SURVEY OF CONSUMER ATTITUDES
AND BEHAVIOR, FALL 1973 (ICPSR 7525). The second section contained
questions of a political nature, focusing on trust in the federal
government and the evaluation of major political institutions, such as
the United States Congress, the Supreme Court, the president, the
military, and the major political parties. The third section elicited
respondents' views of selected institutions, including big business,
local, state, and federal governments, churches, schools, colleges and
universities, labor unions, and the media. The fourth section assessed
general attitudes toward philanthropy and the effects of the growth of
public philanthropy on private giving. Respondents were also asked to
what extent their contributions to charity were, or would be, affected
by recent changes in tax law such as the elimination of tax
breaks. Demographic variables include sex, age, ethnicity, marital
status, level of education, marital status, duration of present and
past marriages, employment status, main occupation, and labor union
membership.
description: The variable V95 (HD&WIFE WEIGHT) is a weight variable which allows for simulation of head wife samples (from the random adult samples) used in the past at ISR. It is created as follows: If R is a married head of hosehold or wife of the head of household, the weight equals the number of eligible respondents in the housing unit. If R is an unmarried head of household, his/her weight is equal to two times the number of eligible respondents in the housing unit. Anyone other than head or wife is assigned a weight of zero. Additional details regarding this weight variable can be found in the codebook.
description: National cross-section sample.
description: Datasets: 
DS1: Omnibus Study, Fall 1973
description: Adult population of the United States, aged 18 and older.
description: computer-assisted personal interview (CAPI); telephone interview
DOI: 10.25326/rge1-x868
title: Atmospheric simulation chamber study: escherichia coli + None - Reference experiment - 2020-07-22
DOI: 10.15156/bio/sh1054073.10fu
title: SH1054073.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1054074.10fu
title: SH1054074.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1054075.10fu
title: SH1054075.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1054076.10fu
title: SH1054076.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1054077.10fu
title: SH1054077.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.