cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/28s2bhdjfd.1
title: CBC Dataset

description: - About Dataset 
Safa S. Abdul-Jabbar, Alaa k. Farhan

- Context
This is the first Dataset for various ordinary patients in Iraq. The Dataset provides the patients’ Cell Blood Count test information that can be used to create a Hematology diagnosis/prediction system. Also, this Data was collected in 2022 from Al-Zahraa Al-Ahly Hospital. These data can be cleaned & analyzed using any programming language because it is provided in an excel file that can be accessed and manipulated easily. The user just needs to understand how rows and columns are arranged because the data was collected as images(CBC images) from the laboratories and then stored the extracted data in an excel file.
	Content
This Dataset contains 500 rows. For each row (patient information), there are 21 columns containing CBC test features that can be described as follows:
1.	ID: Patients Identifier
2.	WBC: White Blood Cell, Normal Ranges: 4.0 to 10.0, Unit: 10^9/L.
3.	LYMp: Lymphocytes percentage, which is a type of white blood cell, Normal Ranges: 20.0 to 40.0, Unit: %
4.	MIDp: Indicates the percentage combined value of the other types of white blood cells not classified as lymphocytes or granulocytes, Normal Ranges: 1.0 to 15.0, Unit: %
5.	NEUTp: Neutrophils are a type of white blood cell (leukocytes); neutrophils percentage, Normal Ranges: 50.0 to 70.0, Unit: %
6.	LYMn: Lymphocytes number are a type of white blood cell, Normal Ranges: 0.6 to 4.1, Unit: 10^9/L.
7.	MIDn:	Indicates the combined number of other white blood cells not classified as lymphocytes or granulocytes, Normal Ranges: 0.1 to 1.8, Unit: 10^9/L.
8.	NEUTn: Neutrophils Number, Normal Ranges: 2.0 to 7.8, Unit: 10^9/L.
9.	RBC: Red Blood Cell, Normal Ranges: 3.50 to 5.50, Unit: 10^12/L
10.	HGB: Hemoglobin, Normal Ranges: 11.0 to 16.0, Unit: g/dL 
11.	HCT: Hematocrit is the proportion, by volume, of the Blood that consists of red blood cells, Normal Ranges: 36.0 to 48.0, Unit: %
12.	MCV: Mean Corpuscular Volume, Normal Ranges: 80.0 to 99.0, Unit: fL 
13.	MCH: Mean Corpuscular Hemoglobin is the average amount of haemoglobin in the average red cell, Normal Ranges: 26.0 to 32.0, Unit: pg
14.	MCHC: Mean Corpuscular Hemoglobin Concentration, Normal Ranges: 32.0 to 36.0, Unit: g/dL
15.	RDWSD: Red Blood Cell Distribution Width, Normal Ranges: 37.0 to 54.0, Unit: fL
16.	RDWCV: Red blood cell distribution width, Normal Ranges: 11.5 to 14.5, Unit: %
17.	PLT: Platelet Count, Normal Ranges: 100 to 400, Unit: 10^9/L
18.	MPV: Mean Platelet Volume, Normal Ranges: 7.4 to 10.4, Unit: fL
19.	PDW: Red Cell Distribution Width, Normal Ranges: 10.0 to 17.0, Unit: %
20.	PCT: The level of Procalcitonin in the Blood, Normal Ranges: 0.10 to 0.28, Unit: %
21.	PLCR: Platelet Large Cell Ratio, Normal Ranges: 13.0 to 43.0, Unit: %
- Acknowledgements
We thank the entire Al-Zahraa Al-Ahly Hospital Hospital team, especially the hospital manager, for cooperating with us in collecting this data while maintaining patients' confidentiality.
DOI: 10.17632/29ntw7sh4r.2
title: Human breast cancer in situ capturing transcriptomics

description: Triplicates from 23 breast cancer patients with luminal a, luminal b, triple-negative, and HER2-positive subtypes. Please refer to metadata.csv for patient, replicate, and subtype associations. All samples were produced using the protocol described in [1]. [1]: http://doi.org/10.1126/science.aaf2403 Changes in Version 2: - Added indices to spot files - Dropped sample with bad spot data (23901_C1)
DOI: 10.17632/82xpnr4jp9.1
title: Data for: Tree rings reveal a major episode of forest mortality in the late 18th century on the Tibetan Plateau

description: The tree-ring database under study are from juniper forests (Juniperus przewalskii Kom. and Juniperus tibetica Kom.). Data from seven sites (DL, DLH, HB, NMT, WL, XS and ZT) was previously described and recently updated by Qi-Bin Zhang's research group. Data from XES was newly developed in this study.
DOI: 10.17632/564j6fm45k.1
title: Extended dataset produced by the Integer sequence (A363743): a(n) = floor(sqrt(log_10(n!))).

description: This integer sequence was registered and published in the On-Line Encyclopedia of Integer Sequences (OEIS.org) Database on August 17 - 2023, under the OEIS code: A363743.

This sequence can be generally expressed as follows: a(n) = floor(sqrt(log_10(n!))), where n is a non-negative integer. It should be noted that the aforementioned formula is written in accordance with OEIS specific style sheet format. On the other hand, it was possible to represent the general formula on another two forms that the following:

1) a(n) = floor(sqrt(A034886(n) - 1)).

2) a(n) = A000196(A034886(n) -1).

This dataset verifies the reported properties in the comments section of the OEIS publication. Our evaluation ranges from 0 to n = 5000, in contrast to the publication which ranges from 0 to n = 92. These mentioned properties are the following: 

* Every non-negative integer occurs at least 4-times.
* Each integer k &gt; 14 appears fewer than k times.
* The only integers k that occur exactly k times are 11, 13 and 14.
* This sequence can produce random values between 0 and 1 if we do a(n)/a(n+m) for any non-negative integer m. 

The numerical data showed on this dataset was generated by the following Mathematica program:

Array[Floor@ Sqrt[Log10[#!]] &, 5000, 0]

The previous program was builded on Mathematica v13.3.0.

Note: More mathematical details, graphics and technical information can be found in the notebook or .nb file provided in this dataset.
DOI: 10.17632/r532stprhv.1
title: Data for: A Systematic Feature Selection Procedure for Short-term Data-driven Building Energy Forecasting Model Development

description: Data for energy modeling for Nesbitt Hall (real building) and reference building (virtual building).
DOI: 10.17632/8bhg7zb2rr.2
title: Anomaly Detection in Hyper-spectral Images Based on Convolutional Neural Networks

description: Algorithm_programs： Programs for the proposed method. Data: Various types of data, including training data, raw data and processed data used for the proposed method. Other methods : Four other methods for comparison, including RX, KRX, CRD and CNND.
DOI: 10.17632/jpzz4gch7z.1
title: A Dataset of Inertial Measurement Units for Handwritten Punjabi Alphabets

description: The dataset consists of Inertial Measurement Unit (IMU) data corresponding to the 41 characters of the Punjabi alphabet. The data was collected using an IMU 6050 sensor, which was attached to a marker held by the participant during the handwriting process. The IMU sensor records accelerations along three axes (X, Y, Z) and rotational velocities along the same three axes, providing a comprehensive view of the motion involved in writing each character.
Data Collection Process: 
Twenty students participated in the data collection process for this study. Each student was tasked with writing all 41 Punjabi characters twice, once with an IMU sensor attached to the upper part of a marker and once with the sensor attached to the lower part. This dual sensor positioning allowed us to examine whether the location of the sensor affects the distinctiveness of the motion patterns recorded for each character. As a result, each student contributed 82 samples (41 characters × 2 sensor positions), creating a comprehensive dataset that captures a diverse array of motion patterns.
The data collection experiment was conducted over four months, ensuring that a substantial volume of data was gathered. During each session, the students wrote the characters on a whiteboard, repeating each character 250 times. To accurately capture the timing and duration of each writing instance, students were provided with a button that they would press before beginning to write a character and release upon completion. The IMU sensor recorded data for all 250 instances of each character written by each student. This extensive data collection approach ensured that multiple repetitions of each character were captured, resulting in a rich dataset that is ideal for analysis and modeling purposes.
Labeling
The data is labeled according to the sequence of the Punjabi alphabet, with each character assigned a unique label. The first character is labeled '1,' the second character '2,' and so on, up to '41.' This labeling allows for easy identification and classification of the characters within the dataset. '1' represents first letter of the Punjabi letter "ਅ" (Ura), '2' represents the second letter "ਆ" (Aira) and so on.
This dataset can be used to develop and train machine learning models, particularly those focused on pattern recognition and handwriting recognition. Researchers and developers can use this data to:
Data Interpretation and Usage:
Character Recognition: Train models to recognize and classify Punjabi characters based on the IMU data.
Sensor Analysis: Study the effect of sensor positioning on the accuracy of character recognition and explore methods to compensate for these variations.
Handwriting Dynamics: Analyze the dynamics of handwriting, such as speed, pressure, and motion trajectories, as recorded by the IMU sensor.

This dataset provides a valuable resource for exploring the use of inertial sensors in handwriting recognition, specifically for the Punjabi alphabet.
DOI: 10.17632/b4mxg7ydb7.3
title: Android permissions dataset, Android Malware and benign Application Data set (consist of permissions and API calls)

description: This dataset consists of apps needed permissions during installation and run-time. We collect apps from three different sources google play, third-party apps and malware dataset. This file contains more than 5,00,000 Android apps. features extracted at the time of installation and execution. One file contains the name of the features and others contain .apk file corresponding to it extracted permissions and API calls. Benign apps are collected from Google's play store, hiapk, app china, Android, mumayi , gfan slideme, and pandaapp. These .apk files collected from the last three years continuously and contain 81 distinct malware families.
DOI: 10.17632/29ntw7sh4r.1
title: Human breast cancer in situ capturing transcriptomics

description: Triplicates from 23 breast cancer patients with luminal a, luminal b, triple-negative, and HER2-positive subtypes. Please refer to metadata.csv for patient, replicate, and subtype associations. All samples were produced using the protocol described in [1]. [1]: http://doi.org/10.1126/science.aaf2403
DOI: 10.15468/dl.tfugbg
title: Occurrence Download

description: A dataset containing 1606 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Prunus cerasus L.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 1606 records from 181 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009314-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/rzhyyzx2z2.1
title: Dataset on utilizing cropping system-based fertilization techniques to improve soil health and crop output while minimizing tillage

description: The dataset addressed tillage techniques such as minimum tillage (MT), conventional tillage (CT), and deep tillage (DT). In terms of nutrient management, NM1 is 100% soil test-based (STB) after fertilizer gradient generation (FRG); NM2 is 125% of STB after fertilizer gradient generation (FRG-2018); NM3 is 100% STB (80% from chemical fertilizers and 20% from cowdung); and NM4 is native fertility (no fertilization). For three consecutive seasonal years (2018–19, 2019–20, and 2020–21), a total of twelve treatments were reproduced three times in accordance with the factorial totally randomized design.
DOI: 10.17632/skrx2fz79n.1
title: Integrating single-cell and spatial transcriptomics to elucidate the cross-talk of SPP1+ macrophage and cancer associated fibroblast in HCC with immune excluded microenvironment

description: we comprehensively analyzed 38,439 cells from six HCC tumor tissues and 45,354 cells from matched adjacent normal tissues to reveal the cellular composition of HCC at single cell resolution. Importantly, we found SPP1+ macrophage in TME was indicative of poor patient survival. Then, we performed ST analysis, importantly we found SPP1+ macrophage colocalized with CAF and wrapped around the edge of tumor. Then we explore the possible interaction between SPP1+ macrophage and malignant hepatocytes and found malignant cells could alter the function of SPP1+ macrophage through hypoxia. We further evaluate the infiltration of SPP1+ macrophage and CAF in several independent HCC cohorts with bulk transcriptomics using our scRNA-seq as reference transcriptomics matrix and revealed a close correlation of the infiltration between these two cell subtypes. In addition, the colocalization of SPP1+ macrophage and CAF could limit the infiltration of immune cells in tumor core through promoting the extracellular matrix expression and stimulating the formation of the desmoplastic region.
DOI: 10.17632/d2swjp5dxz.1
title: origami isolator 3d cad

description: this data corresponded to model used in the paper titled "Novel Nonlinear Suspension based on the Concept of
Origami Metastructures: Theoretical and Experimental Investigations" by Antonio Zippo et. alt
DOI: 10.17632/rgpddwxrx5.1
title: Foggy License Plates Worldwide: A Comprehensive Dataset

description: The "Foggy License Plates Worldwide" dataset is a specialized collection designed to advance the recognition and detection of vehicles and license plates under foggy conditions. This dataset includes 4420 2D-RGB images, featuring a diverse array of vehicles from Bangladesh, Thailand, Saudi Arabia, and other regions with English license plates. These images are derived from a secondary dataset and have been augmented using monocular depth estimation to simulate varying degrees of fog, offering a realistic set of data for challenging visibility scenarios.

The dataset is not limited to one type of vehicle but includes buses, trucks, CNG vehicles, motorcycles, cars, and standalone license plates, providing a broad spectrum for analysis. While the Bangladeshi subset contains 2754 annotated images, the dataset also includes 388 images from Thailand, 433 from regions with English license plates, and 845 from Saudi Arabia, though the latter does not come with annotations.

This variety is crucial for developing robust algorithms that can operate across different regions and vehicle types, especially in foggy conditions. The use of the Monodepth2 Network to artificially introduce fog effects based on depth estimation ensures that the dataset can mimic real-world scenarios, enhancing the development of automated systems for license plate recognition, vehicle detection, and traffic monitoring under adverse weather conditions. By offering a global perspective with plates from multiple countries, this dataset serves as an invaluable resource for researchers and developers in the field of computer vision, aiming to enhance the accuracy and reliability of systems in foggy environments.
DOI: 10.17632/3h8fwbt3p6.1
title: Dominant Currency Debt: Replication Package

description: Replication Package for "Dominant Currency Debt" by Egemen Eren and Semyon Malamud - Journal of Financial Economics
DOI: 10.17632/y8bb27f9sy.3
title: Survey data on Consumer Behaviour: A study on consumer awareness level.

description: A consumer survey was conducted in Uttar Pradesh in five major cities Lucknow, Kanpur, Agra, Varanasi, and Meerut District, from 2022 to 2023 to understand consumer awareness and consumer behaviour towards Sustainable Apparel. The data were collected from 384 respondents via face-to-face interviews through field surveys in different shopping malls.
DOI: 10.17632/gfc32vndz8.2
title: Synthetic Arabic Data for Scene Text Recognition

description: The dataset consists of 50,000 cropped images with embedded Arabic text. The labels were generated from an Arabic words corpus, which consists of 15 thousand words. The second dataset was collected from Twitter Arabic hashtags and contins 100 cropped images. The datasets were used in our publuished paper "Detecting Spam Images with Embedded Arabic Text in Twitter".
DOI: 10.17632/svrvkfbnkr.1
title: Fiscal data for Japan

description: The data consists of three variables: tax revenues, government expenditure and GDP of Japan over the 1960-2016 period. The revenue variable is defined as the total tax revenues, including direct and indirect taxes, less social security contributions. The expenditure variable is defined as the total government expenditure less net interest payments. All these variables are expressed in logarithm, per capita, and real terms. The data was collected from OECD’s Economic Outlook Projections database.
DOI: 10.17632/nm9dxh99x9.1
title: Data for: MACHINE LEARNING AND NONLINEAR MODELS FOR THE ESTIMATION OF FUNDAMENTAL PERIOD OF VIBRATION OF MASONRY INFILLED RC FRAME STRUCTURES

description: Accurate estimation of the fundamental period of vibration of masonry infilled RC frame structures using both Machine Learning techniques and concise nonlinear formulas.
DOI: 10.17632/5yymds8v5b.1
title: Scripts for  calculating the average values of DTI and NODDI metrics

description: This is an in-house python script to compute the mean DTI and NODDI metrics values over the tract by averaging the measurements from all sampling points of all fibers.
DOI: 10.17632/8d6wvt6cnc.1
title: Sustained endocrine and exocrine function in the pancreas of the Pacific spiny dogfish

description: 
Secretions of the exocrine pancreas contain digestive enzymes integral to the digestive process. The Pacific spiny dogfish (Squalus suckleyi) has a discrete pancreas, divided into two lobes termed the dorsal and ventral lobes. These lobes drain into the anterior intestine via a common duct to enable digestion. Previous studies have identified that the exocrine pancreas produces (co)lipases, chymotrypsin, carboxypeptidase, and low levels of chitinases, however investigations into other digestive enzymes are limited. We detect the presence of lipase, trypsin and carbohydrase and show that activities are equivalent between both lobes of the pancreas. Additionally, we sought to investigate the influence of a single feeding event (2% body weight ration of herring by gavage) on enzyme activities over an extended time course (0, 20, 48, 72, 168 h) post-feeding. The results indicate that there are no differences in pancreatic tissue digestive enzyme activities between fed or fasted states. Analysis of acinar cell circumference post-feeding demonstrates a significant increase at 20 and 48 h, that returns to fasting levels by 72 h. No significant changes were observed regarding whole-tissue insulin or glucagon mRNA abundance or with glucose transporter (glut) 1 or 3. Yet, a significant and transient decrease in glut4 and sodium glucose-linked transporter mRNA abundance was found at 48 h post-feeding. We propose that the constant enzyme activity across this relatively large organ, in combination with a relatively slow rate of digestion leads to an evenly distributed, sustained release of digestive enzymes regardless of digestive state.
DOI: 10.17632/72j6brn9st.1
title: Bimekizumab safety in moderate to severe plaque psoriasis: Rates of hepatic events and changes in liver parameters over 2 years in randomized phase 3/3b trials – Supplementary Materials

description: Supplementary Materials for the manuscript titled 'Bimekizumab safety in moderate to severe plaque psoriasis: Rates of hepatic events and changes in liver parameters over 2 years in randomized phase 3/3b trials'.

Includes a plain language summary (PLS) of the manuscript, a summary of hematology and biochemistry assessment schedules (Supplementary Table 1), EAIRs of laboratory elevations in ALT or AST &gt;3x and &gt;5x ULN with bimekizumab, placebo, and active comparators (Supplementary Table 2), and a summary of relevant medical history, concomitant medications, and confounding factors in patients with instances of laboratory elevations in ALT or AST &gt;5x ULN (Supplementary Table 3).
DOI: 10.17632/nm2gpv52c2.1
title: On the Probabilistic Prediction for Extreme Geometrical Defects Induced by Laser-Based Powder Bed Fusion

description: Research Paper Abstract:
Compared to traditional subtractive manufacturing processes, powder bed fusion (PBF) shows promise for making complex metal parts with design freedom, short development time, and environmental sustainability. However, there is a consensus within the additive manufacturing (AM) community that the random geometrical defects (e.g., porosity, lack-of-fusion) produced in PBF processes pose a great challenge to fabricating load-bearing parts, particularly under dynamic loading conditions. Therefore, it is imperative to quantify defect sizes and distributions to estimate the critical, life-limiting defect size that significantly reduces fatigue life. This paper presents a comprehensive analysis of the defects induced by selective laser melting to quantify their sizes and statistical distributions. Then four cumulative distribution functions (i.e., Weibull, Gamma, Gumbel, and Lognormal CDFs) are leveraged and compared to predict the maximum defect size based on the principle of statistics of extremes. Both the peak over threshold (POT) approach and the block maxima (BM) approach are used for these predictions. The results show that the BM approach-based predictions for all CDFs to be much larger than the measured maxima while the POT approach-based predictions have less deviation. The Weibull and Gamma CDFs were best correlated to the data, measured by Pearson’s R correlation coefficient, while the Gumbel and Lognormal CDFs were also well correlated

About this data:
Geometrical defects from a bulk sample of SLMed SS-316L were measured using the Feret caliper diameter method. The FC diameter defect size was quantified by inscribing each defect within the smallest circle possible and using the resulting diameter as an effective defect size. The grouping or clustering effect of defects was not accounted for in the size of the defects. Only individual defects were measured to simplify this study. A total of 769 defects were collected and measured. A Keyence VR 3100 optical microscope was used for these measurements.
To observe these defects, 14 sample slices of the bulk printed sample were obtained. Only the defects within and touching the border of a 5 mm x 5 mm area of the sample slices (from both sides of the sliced sample) were measured. This is because these areas corresponded to gauge sections of the sample slices., which can be used for fatigue testing. Therefore the gauge sections are the regions from which cracks are expected to propagate due to the higher stresses that arise from the reduction in specimen cross section. This means all defects outside of these regions are not as critical and are not included in the distribution.
The attached files denote the sample number (1-14) and the sample side (top/bottom face) to which the data correspond.