cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/y4wsb24xc3.2
title: Stabilometry and moca data
DOI: 10.17632/f6fxxdgpxg.1
title: Cedrus atlantica pollen FTIR spectra

description: This dataset contains FTIR spectra of modern Cedrus atlantica pollen samples collected in Morocco, and from Europe and the USA, analysed on a PerkinElmer Spotlight 400 imaging system using a Universal Attenuated Total Reflection (UATR) accessory in the Earth Science Laboratories at The University of Manchester. Please see the "Sample details" file for full information about each sample: This spreadsheet contains details about every pollen sample used in this study. This includes the tree’s location (longitude, latitude, and altitude), the aspect of the tree, i.e. the direction of the slope in degrees, the slope angle, the tree’s diameter at breast height in cm (DBH), the height of the tree in metres, the height at which the strobili (pollen cone) was picked from the tree, and the number of other trees within a 5 metre radius of the sampled tree. The file is organised with Moroccan samples in one table, followed by non-Morocco samples. The available information on samples collected from the Rif Mountains, and those from outside Morocco differs from Moroccan samples. Information on non-Morocco samples includes provenance (where known) and the ID code used by the botanical garden. FTIR spectra: There are three files which contain the complete FTIR spectra for every pollen sample (including replicates) used in this study. The original uncorrected FTIR spectra, baseline-corrected spectra, and z-score standardised spectra are available. These are detailed in the methods section of the associated paper (https://doi.org/10.1177/0959683618777072). Numbers across the top (columns) represent the wavenumber, and the observations are relative absorbance values. Nitrogen data: Most of the pollen samples were also analysed on a CHNS analyser (Symes 2017, unpublished MSc dissertation). Untreated pollen was analysed with 3 replicates. The N values available in the file are an average of the replicates and are shown as a percentage.
DOI: 10.17632/h58s544674.1
title: Tourism Dataset

description: This dataset contains tourist visit transactions at three popular tourist locations in Indonesia, namely Bali, Malang, and Yogyakarta. The data contains the historical tourism experiences recorded on the TripAdvisor website that have been collected from October 2022 to January 2023. This dataset is provided to support the researchers, especially in the field of tourism and soft computing, and not for commercialization purposes.
DOI: 10.17632/wmf22zwngt.1
title: Data for: Single Mothers and their Children: Evaluating a Work- Encouraging Welfare Reform

description: These files provide all the codes used to merge and clean datasets and run results of the paper
DOI: 10.17632/2h6jmgdsmd.1
title: Intergenerational Transmission of Political Status in China

description: Stata codes and data set
DOI: 10.15468/dl.98fhvh
title: Occurrence Download

description: A dataset containing 342 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Prosopis alba Griseb.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 342 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009296-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/9h2y3z8x5r.1
title: WoW - Wearable Respiration Monitoring Dataset

description: This dataset was acquired by the Institute of Systems and Robotics, University of Coimbra, as a part of the CMU-Portugal WoW project, Wireless biOmonitoring stickers and smart bed architecture: toWards Untethered Patients (https://inovglintt.com/financiamento/wow/). The main objective of the project is to develop wearable devices for remote and continuous patient monitoring. As a part of this project, we focused on respiration data, to evaluate methods for estimation of human respiration rate through simple and low-cost wearable patches. We are as well interested to understand how body motion, and position (sitting/standing/moving) affects the accuracy of the estimation. Hence, we have recorded data from different sensor inputs. This includes printed, thin-film, and stretchable skin-mounted strain gauges installed on different body locations, bioimpedance measurements via skin-interfacing electrodes, and a chest/belly-mounted accelerometer. This dataset contains respiration data from 4 subjects. In all measurement sessions, we also acquired ground truth data via a nose-mounted thermistor. The dataset can be used along with sensor fusion and machine learning techniques, in order to develop algorithms that can precisely estimate the respiration rate, during different body positions and activities.
DOI: 10.17632/prs6zjts7b.1
title: SARS-CoV-2 variants evolve convergent strategies to remodel the host response

description: Publication
"SARS-CoV-2 variants evolve convergent strategies to remodel the host response" by Bouhaddou et al.

Abstract
SARS-CoV-2 variants of concern (VOCs) emerged during the COVID-19 pandemic. Here, we used unbiased systems approaches to study the host selective forces driving VOC evolution. We discovered VOCs evolved convergent strategies to remodel the host by modulating viral RNA and protein levels, altering viral and host protein phosphorylation, and rewiring virus-host protein-protein interactions. Integrative computational analyses revealed that although Alpha, Beta, Gamma, and Delta ultimately converged to suppress interferon-stimulated genes (ISGs), Omicron BA.1 did not. ISG suppression correlated with expression of viral innate immune antagonist proteins, including Orf6, N and Orf9b, which we mapped to specific mutations. Later Omicron subvariants BA.4 and BA.5 more potently suppressed innate immunity than early subvariant BA.1, which correlated with Orf6 levels, though muted in BA.4 by a mutation that disrupts the Orf6-nuclear pore interaction. Our findings suggest SARS-CoV-2 convergent evolution overcame human adaptive and innate immune barriers, laying the groundwork to tackle future pandemics.

Tables
RNA (tables 1), peptide (table 3, 6, 7), and protein (tables 4) counts and quantitative statistical comparisons (e.g. log2 fold changes and p-values; tables 2, 5, 8) between conditions, across both host and viral genes, during infection.
Table 1. Quantification of host RNA transcript counts in transcripts per million during infection, related to Figure 2.
Table 2. Differential host RNA expression analysis comparing conditions during infection, related to Figures 1 and 4.
Table 3. Quantification of host and viral proteins using abundance proteomics at the feature level, related to Figures 2 and 5.
Table 4. Quantification of host and viral proteins using abundance proteomics at the protein level, related to Figures 2 and 5.
Table 5. Differential host protein expression analysis comparing conditions during infection, related to Figures 1 and 4.
Table 6. Quantification of host and viral phosphorylated peptides using phosphoproteomics at the feature level, related to Figure 2.
Table 7. Quantification of host and viral phosphorylation sites using phosphoproteomics summarized at the site level, related to Figure 2.
Table 8. Differential host phosphorylation site intensity analysis comparing conditions during infection, related to Figures 1 and 4.

Supplemental Tables
Table S1. Consensus mutations in the VOCs from CoVariants.org, related to Figure 1.
Table S2. Viral RNA, protein, and phosphorylation site abundances during infection, including analysis comparing viral phosphorylated peptide intensities across VOCs, related to Figures 2 and 5.
Table S3. Affinity purification mass spectrometry (APMS) metadata and results, related to Figure 3.
Table S4. Integrative computational analysis metadata and results, related to Figure 4.
DOI: 10.17632/m2csf3x6rw.1
title: ODI batting strike rate

description: Batting strike rate of ODI men's cricket matches played between 2005 and 2022/
DOI: 10.17632/h8y47z5hc9.1
title: The Hare and the Baboon: Intersecting Violences Experienced by African Sexual and Gender-Expansive Individuals in the UK Asylum System.
DOI: 10.17632/w82y3mmx4h.1
title: Dataset of Development Policy in ASEAN (1965-2019)

description: The development policy in ASEAN research & publication dataset, which was indexed by Scopus from 1965 to 2019. The dataset contains data authors, authors ID Scopus, title, year, source title, volume, issue, article number in Scopus, DOI, link, affiliation, abstract, index keywords, references, Correspondence Address, editors, publisher, conference name, conference date, conference code, ISSN, language, document type, access type, and EID.
DOI: 10.17632/7s3ys9ntnz.1
title: Data for: Machine learning for inventory management: Analyzing two concepts to get from data to decisions

description: Over two years of daily sales data from a restaurant in Stuttgart, Germany. Number of servings sold for 7 products (CALAMARI, FISCH, GARNELEN, HAEHNCHEN, KOEFTE, LAMM, STEAK). Features include calendar informations such as weekday or special events, information derived from historical sales as well as weather from the local weather station.
DOI: 10.17632/b9mck42r35.1
title: neuroGPT-X: Towards an Accountable Expert Opinion Tool for Vestibular Schwannoma

description: We hypothesize that a well-trained, context-enriched GPT will perform at the level of or better than an expert surgeon in generating comprehensive answers to questions surrounding commonly posed in day-to-day practice regarding vestibular schwannoma. In this study, we make three key contributions to assessing the feasibility of LLMs as a clinical decision-making adjunct.

1. We develop a framework to context-enrich GPT with context relevant to vestibular schwannoma.
2. We compare the performance of ChatGPT (Jan. 30, 2023 model) and a context-enriched GPT model against leading neurosurgical experts worldwide, evaluating the ability of large language models (LLMs) to assist in clinical decision-making.
3. We introduce a proof-of-concept clinical decision-making tool, neuroGPT-X, which incorporates working memory, sources with each answer, and a web-based chat platform to address challenges in using LLMs in a clinical setting, including interpretability, reliability, accountability, and safety.
DOI: 10.17632/wk99xcjv59.1
title: EndNote Study Dataset (1973-2018)

description: The EndNote reseach & publication dataset, which was indexed by Scopus from 1973 to 2018. The dataset contains data authors, authors ID Scopus, title, year, source title, volume, issue, article number in Scopus, DOI, link, affiliation, abstract, index keywords, references, Correspondence Address, editors, publisher, conference name, conference date, conference code, ISSN, language, document type, access type, and EID.
DOI: 10.17632/p3v94dft2y.2
title: Ransomware PE Header Feature Dataset

description: This dataset contains headers of 2157 binary executable samples comprising 1134 legitimate software (goodware) and 1023 ransomware, grouped into 25 ransomware families. The dataset was retrieved by extracting raw information of the PE header (first 1024 bytes).

The CSV file columns are sample ID, filename, target class (GR), family ID, and numerical columns from 0 to 1023, as follows:
			| 	ID			| 	filename			| GR	| 	family	| 			0 - 1023					|
Goodware	| 10000 to 11133	| Their name.exe		| 0	| 0			| Numerical features ranging from 0 to 255	|
Ransomware	| 20000 to 21022	| Their SHA-256 hash	| 1	| 25 family IDs	| Numerical features ranging from 0 to 255	|

Family IDs:
Avaddon			1
Babuk			2
Blackmatter		3
Conti			4
Darkside			5
Dharma			6
Doppelpaymer	7
Exorcist			8
Gandcrab		9
Lockbit			10
Makop			11
Maze			12
Mountlocker		13
Nefilim			14
Netwalker		15
Phobos			16
Pysa				17
Ragnarok		18
RansomeXX		19
Revil			20
Ryuk			21
Stop				22
Thanos			23
Wastedlocker		24
Zeppelin			25

A complete description of this dataset can be found in the paper "Moreira, C.C., Moreira, D.C., Sales Jr., C. de S. de:
Improving Ransomware Detection based on Portable Executable Header using Xception Convolutional Neural Network. Computers & Security, 103265 (2023), DOI: https://doi.org/10.1016/j.cose.2023.103265, URL: https://www.sciencedirect.com/science/article/pii/S016740482300175X "

Please, reference our work when using this dataset.
DOI: 10.17632/sc7hvdzc94.3
title: Supplementary Material for "Comparative efficacy of biologics and oral agents in palmoplantar psoriasis and palmoplantar pustulosis: A systematic review and network meta-analysis of randomized clinical trials"

description: Supplementary tables and figures for the paper titled "Comparative efficacy of biologics and oral agents in palmoplantar psoriasis and palmoplantar pustulosis: A systematic review and network meta-analysis of randomized clinical trials".
DOI: 10.17632/989gp9rhtb.1
title: Data for: No glory without sacrifice – drivers of climate (in)action in the general population

description: Data are shared in csv-format and consist of 12 columns and 499 rows (with headings). The first line represents the respective index. The following columns describe the eight independent and three dependent variables of the regression models. “FOP_mean” is the mean index of the statements concerning feelings of powerlessness, “SAC_mean” is the mean index of the statements concerning willingness to sacrifice, “concern_mean” is the mean index of the statements regarding concern. “Knowledge” gives the accuracy scores without guessed answers for the ten climate statements that had to be answered. Gender is binary coded with female (0), male (1), not defined/no answer (NA). “age” is a metric variable, ranging from 18 to 72. “matura” is binary coded: without matura/high school degree (0), with matura/high school degree (1), not defined/no answer (NA). “partiesLR” represents left-wing (0) and right-wing (1) parties, not defined/no answer (NA). “conser_mean”, “social_mean” and “citizen_mean” represent mean indices for the behavior scales “conservation behavior”, “social climate protection” and “climate citizenship”. Data columns not relevant for this analysis were removed. Some variables have been recoded or were calculated from other variables. Further description of the data can be found in the method section of the paper. Data were collected in cooperation with the market research agency Norstat Deutschland GmbH.
DOI: 10.17632/jrnnst4wnc.1
title: Sustainable Planning of Precious and Rare Elements’ Recycling From Waste Electronic Products 

description: A Waste Electrical-Electronic Equipment (WEEE) recycling company is taken into account which operates in Turkey. It is assumed that WEEE are collected from 7 regions of Turkey, from a pre-determined location of each region. Data was collected from different resources about the collected/recycling amounts of products on a regional basis, the amount of precious metals obtained from recycling operations, operational and logistic costs, labor costs and the amount of CO2 emitted during the recycling processes and logistics. 
Annual collection targets for different kinds of WEEE (especially for electronic product wastes) were determined by the Ministry of Environment for different future years. 
The data for the amount of rare and precious element from each type of electronic product was collected from the literature. Besides, the data of the amount of toxic materials in each type of products was taken from the literature.
The distances from the center of the geographical regions to the facilty were obtained from Google Maps. 
For the amount of emissions due to the logistics of the WEEE into the facility, for a selected truck type, its fuel consumption for a given payload in these conditions were taken from the associated open data sources. 
Other scalar and parametric data are explained in detail in the appended file. 
Some sensitive cost data is not revealed due to the privacy issues.
DOI: 10.17632/6fx22vj6g6.1
title: Dataset of tweets in English language about the COVID-19 pandemic for binary sentiment analysis

description: This dataset is aimed to the task of sentiment analysis in tweets about the COVID-19 pandemic. There are 3 versions of the dataset,  composed by 186,000, 132,000, and 82,000 tweets in English language with stopwords removal, respectively. Positive tweets have polarity equal to 1, while negative tweets have polarity equal to 0 in all versions.
All datasets were selected, cleaned and organized from the public dataset available at <https://ieee-dataport.org/open-access/coronavirus-covid-19-tweets-dataset>.
The datasets are accompanied by embedding matrices generated from the pre-trained Word2Vec shallow neural network available at <https://data.mendeley.com/datasets/t8bxg423yk/1>.
DOI: 10.17632/zysvgmxcyz.1
title: Comprehensive Dataset on Ripening Stages of Strawberries and Avocados: From Unripe to Rotten

description: This dataset presents introduces a unique and innovative fruit images dataset of 14,630, including 1,333 original images and the rest of the augmented images for the fruits of strawberries and avocados. The dataset captures the development of strawberries and avocados through four distinct stages: unripe, partially ripe, ripe, and rotten. The fruits were sourced from farms in Mahabaleshwar, Maharashtra, India, and local markets in Pune and Maharashtra. The fruits were regularly monitored from the harvesting stage, with all noticeable changes carefully documented.
DOI: 10.17632/zz74tx28j2.1
title: Exploring Factors influencing the Online Impulse Buying Behavior of Students on TikTok Shop

description: The dataset aims to explore the factors influencing the online impulse buying behavior of university and college students in Can Tho, a central city in the Mekong Delta of Vietnam, on TikTok Shop. Data collection was conducted from January 2024 to March 2024 via an online questionnaire, resulting in 361 valid responses.
DOI: 10.17632/4tp83br92z.1
title: Underwater Species Dataset (NR) from Video: Seals, Dolphins, Sea Turtles, Octopus, Seahorse, Sharks, Whales

description: This dataset comprises 1728 images extracted from underwater footage sourced from a specified YouTube video (https://www.youtube.com/watch?v=eoTpdTU8nTA), focusing on seven distinct marine species: Seals, Dolphins, Sea Turtles, Octopus, Seahorse, Sharks, and Whales. It includes 1291 training images, 143 test images, and 294 validation images, meticulously annotated for supervised learning tasks. This dataset is designed to support research in underwater species detection, classification, and behavior analysis, as well as to advance computer vision algorithms tailored for underwater environments. The dataset's availability aims to foster advancements in marine biology, conservation efforts, and technological innovations in underwater imaging and analysis.
DOI: 10.17632/57dkchhjbp.1
title: Procrustes: A python library to find transformations that maximize the similarity between matrices

description: We have developed Procrustes, a free, open-source, cross-platform, and user-friendly Python library implementing a wide-range of algorithmic solutions to Procrustes problems. The goal of Procrustes analysis is to find an optimal transformation that makes two matrices as close as possible to each other, where the matrices are often (but need not always be) a list of multidimensional points specifying the systems of interest. We demonstrate the functionality of the package through various examples, mostly from cheminformatics. However, Procrustes analysis has broad applicability including image recognition, signal processing, data science, machine learning, computational biology, chemistry, and physics. Our library includes methods for one-sided Procrustes problems using orthogonal, rotational, symmetric, and permutation transformation matrices, as well as two-sided Procrustes problems using orthogonal and permutation transformation matrices. For the two-sided permutation Procrustes problem, we include heuristic algorithms along with a rigorous (but slow) method based on softassign. In addition, we include a general formulation of the Procrustes problem. The Procrustes source code and documentation is hosted on GitHub (https://github.com/theochem/procrustes).
DOI: 10.17632/9b45k4hkdf.1
title: Android Permissions Dataset

description: This dataset contain a bulk number of extracted permissions from Android applications
DOI: 10.17632/vcz5npdw9v.1
title: Supplementary material - Baricitinib combination treatment for severe to very severe alopecia areata

description: Comparison of the SALT scores between patients treated with monotherapy and those who received coadjuvant treatment.
DOI: 10.17632/hb4p6y66rc.1
title: Tourist Risk Perception

description: US sample of tourists who have used their passports in the past 5 years (M-Turk). The study investigates motivational traits and risk appraisal effects on travel destination risk perception. Illustrative hypothesis: expectations of vulnerability to Covid-19 has a positive influence on destination risk perception.
DOI: 10.17632/fjbydyvpy6.1
title: Data for: Household risk perceptions and evacuation behavior in earthquake and tsunami in a Cascadia Subduction Zone coastal city.

description: This dataset is a result of a household survey conducted in the Fall 2017 in Seaside, Oregon. It contains data on public risk perceptions and behavioral intentions in the threat of Cascadia Subduction Zone Earthquake and Tsunami.
DOI: 10.17632/534yf9j6zx.1
title: Flow chart of the DRESS CODE Study

description: The trial was stopped prematurely (31/12/2017) owing to poor recruitment. In the end, 52 patients underwent randomization, 26 in each treatment group
DOI: 10.17632/9srgy5rhsb.1
title: Supplementary 1 and 2

description: Details of propensity score matching process
DOI: 10.17632/w2r855hpx8.2
title: SoyNet: Indian Soybean Image dataset with quality images captured from the agriculture field ( healthy and disease Images)

description: High-quality images of soybean leaf are required to solve soybean disease and healthy leaves classification and recognition problems. To build the machine learning models, deep learning models with neat and clean dataset is the elementary requirement in research. With this objective, this data set is created, which consists of healthy and disease-quality images of soybean named “SoyNet”. This dataset consists of 9000+ high-quality images of soybeans (healthy and Disease quality) with different angles and Images captured direct from the soybean agriculture field to analyze the real problem in research. The images are divided into 2 sub-folders 1) Raw SoyNet Data and 2) Pre-processing SoyNet Data. Each Sub folder contains a digital camera Click, which contains healthy and disease image folders, and 2) Mobile Phone Click, which contains disease images. The Pre-processing SoyNet Data contains folders of 256*256 resized images and grayscale images in a similar manner to disease and healthy data. A Digital-Camera and a Mobile phone with a high-end resolution camera were used to capture the images. The images were taken at the soybean cultivation field in different lighting conditions and backgrounds. The proposed dataset can be used for training, testing, and validation of soybean classification or reorganization models.