cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/icpsr38793.v1
title: Uniform Crime Reporting Program Data: Property Stolen and Recovered, United States, 2020
title: Version 1

description: The UNIFORM CRIME REPORTING PROGRAM DATA: PROPERTY STOLEN AND RECOVERED, UNITED STATES, 2020 file (also known as the Supplement to Return A) is collected at the agency level and includes detailed monthly data on the nature of crime and the value and type of property stolen and recovered incident to each crime. The Return A Supplement requires that a value be established for property stolen and recovered in each Crime Index category except aggravated assault. It is designed to record the value of property stolen and recovered in the following eleven classifications: Currency/notes, Jewelry and Precious Metals, Clothing and Furs, Locally Stolen Motor Vehicles, Office Equipment, Televisions/Radios, Firearms, Household Goods, Consumable Goods, Livestock, and Miscellaneous. The determination of the value of property stolen is an obligation of the investigating officer, and such information is essential to assure the completeness of a law enforcement investigative report on stolen property. The data were originally assembled by the Federal Bureau of Investigation (FBI) from reports submitted by agencies participating in the UCR. The ICPSR file was processed from Return A Supplement files provided by the FBI.
description: Datasets: 
DS1: Uniform Crime Reporting Program Data: Property Stolen and Recovered, United States, 2020
description: Property crimes reported by law enforcement agencies in the United States.
description: self-enumerated questionnaireStarting with the year 1998, each of the four parts of the UCR data collection archived by ICPSR is released as a separate study under its own study number. The Property Stolen and Recovered data for the years 1975-1997 can be found in UNIFORM CRIME REPORTING PROGRAM DATA: 1975-1997 (ICPSR 9028).These studies are 'static data snapshots' of the data available on the FBI website via the Crime Data Explorer, which is continuously updated. For additional information on the Uniform Crime Reporting (UCR) Program, please visit the UCR website.
DOI: 10.5281/zenodo.10364383
title: Immune cell parameters found to be impacted in patients with autoimmune and autoinflammatory disorders enrolled in the Transimmunom study.

description: Expanding upon the Transimmunom clinical trial, we gathered peripheral blood samples from 443 patients enrolled between 2015 and 2022, who were afflicted with 15 distinct autoimmune or autoinflammatory diseases or related conditions. Additionally, blood samples were collected from 71 healthy volunteers to establish a reference condition. The screened disorders spanned a spectrum from purely autoinflammatory to purely autoimmune diseases, encompassing various levels of disorder activities without a specific threshold. The disorders examined included arthritis conditions such as Behçet's disease (BD; n=38), knee osteoarthritis (OA; n=45), rheumatoid arthritis (RA; n=91), spondylarthritis (SA; n=58), and systemic lupus erythematosus (SLE; n=33). Blood vessel disorders, such as Antiphospholipid syndrome (APLS; n=23), Churg-Strauss's disease (CS; n=6), Granulomatosis with polyangiitis (GPA; n=14), and Takayasu arteritis (TA; n=22), were also included. Metabolic disorders like Type 1 Diabetes (T1D; n=60) and Type 2 Diabetes (T2D; n=27), muscle disorder (Myositis; n=4), inflammatory bowel diseases (IBD) like Crohn's disease (CD; n=10) and ulcerative colitis (UC; n=5), as well as IBD-like diseases including Familial Mediterranean Fever (FMF; n=7), were part of the study.
To analyze the patient blood samples, we utilized 12 flow cytometry panels, each consisting of 10 cell markers. We introduced an extra panel specifically designed to identify the primary immune cell populations. This panel featured numeration beads, enabling the determination of absolute counts for all populations. It served as a reference tube, facilitating the calculation of absolute counts in all other panels through extrapolation from shared populations. In total, we analyzed more than 600 innate and adaptive immune cell parameters (both absolute cell counts and percentages) from patient's blood.
The provided data encompasses significant flow cytometry data related to the Transimmunom study. This dataset specifically details the values of 224 cell parameters that exhibit significant differential abundance in at least one disease when compared to samples from healthy volunteers. Both Cliff's Delta effect size and two-way non-parametric Wilcoxon test were used to identify cell parameters significantly differentially abundant between conditions. Cell parameters with an absolute value of Cliff's Delta effect size higher than 0.33 and with a p-value lower than 0.05 were considered statistically significant. 
The provided file is formatted as a tab-separated file. Each row corresponds to a specific cell parameter, either representing cell percentage or absolute cell count. Each column corresponds to a distinct patient, identified by the disease name and anonymous identifier.
DOI: 10.5281/zenodo.10364382
title: Immune cell parameters found to be impacted in patients with autoimmune and autoinflammatory disorders enrolled in the Transimmunom study.

description: Expanding upon the Transimmunom clinical trial, we gathered peripheral blood samples from 443 patients enrolled between 2015 and 2022, who were afflicted with 15 distinct autoimmune or autoinflammatory diseases or related conditions. Additionally, blood samples were collected from 71 healthy volunteers to establish a reference condition. The screened disorders spanned a spectrum from purely autoinflammatory to purely autoimmune diseases, encompassing various levels of disorder activities without a specific threshold. The disorders examined included arthritis conditions such as Behçet's disease (BD; n=38), knee osteoarthritis (OA; n=45), rheumatoid arthritis (RA; n=91), spondylarthritis (SA; n=58), and systemic lupus erythematosus (SLE; n=33). Blood vessel disorders, such as Antiphospholipid syndrome (APLS; n=23), Churg-Strauss's disease (CS; n=6), Granulomatosis with polyangiitis (GPA; n=14), and Takayasu arteritis (TA; n=22), were also included. Metabolic disorders like Type 1 Diabetes (T1D; n=60) and Type 2 Diabetes (T2D; n=27), muscle disorder (Myositis; n=4), inflammatory bowel diseases (IBD) like Crohn's disease (CD; n=10) and ulcerative colitis (UC; n=5), as well as IBD-like diseases including Familial Mediterranean Fever (FMF; n=7), were part of the study.
To analyze the patient blood samples, we utilized 12 flow cytometry panels, each consisting of 10 cell markers. We introduced an extra panel specifically designed to identify the primary immune cell populations. This panel featured numeration beads, enabling the determination of absolute counts for all populations. It served as a reference tube, facilitating the calculation of absolute counts in all other panels through extrapolation from shared populations. In total, we analyzed more than 600 innate and adaptive immune cell parameters (both absolute cell counts and percentages) from patient's blood.
The provided data encompasses significant flow cytometry data related to the Transimmunom study. This dataset specifically details the values of 224 cell parameters that exhibit significant differential abundance in at least one disease when compared to samples from healthy volunteers. Both Cliff's Delta effect size and two-way non-parametric Wilcoxon test were used to identify cell parameters significantly differentially abundant between conditions. Cell parameters with an absolute value of Cliff's Delta effect size higher than 0.33 and with a p-value lower than 0.05 were considered statistically significant. 
The provided file is formatted as a tab-separated file. Each row corresponds to a specific cell parameter, either representing cell percentage or absolute cell count. Each column corresponds to a distinct patient, identified by the disease name and anonymous identifier.
DOI: 10.17632/v9ndkpy8zp.1
title: JAP dataset

description: N/A
DOI: 10.17632/gc233nkjgk.1
title: Chakma Language POS Tagging Dataset

description: The Chakma Language POS Tagging Dataset is a valuable linguistic resource designed for the analysis and understanding of the Chakma language. Chakma is a member of the Indo-Aryan language family and is primarily spoken by the Chakma people in the Chittagong Hill Tracts region of Bangladesh and in parts of India and Myanmar. This dataset aims to facilitate research and development in Chakma language processing, particularly in the domain of Part-of-Speech (POS) tagging. Bengali: This column contains sentences and phrases in the Bengali script. Bengali is used for representing Chakma text in this dataset. Chakma (Character): In this column, Chakma words or characters are presented in their native script. Chakma script is an abugida script used for writing the Chakma language. Bengali (Chakma): This column provides a transliteration of Chakma words or characters into the Bengali script. It enables users who are familiar with Bengali to understand and work with the Chakma text more easily. Parts of Speech (POS): The Parts of Speech column contains POS tags assigned to each word or character in the Chakma language. POS tagging is a crucial linguistic task that assigns grammatical categories (e.g., noun, verb, adjective) to each word in a text, enabling syntactic and semantic analysis. Usage: Linguistic Analysis: Researchers and linguists can use this dataset for linguistic analysis, syntactic studies, and language documentation of the Chakma language. Natural Language Processing (NLP): NLP practitioners can leverage this dataset to build POS tagging models for Chakma, aiding in machine translation, sentiment analysis, and other NLP tasks. Language Preservation: This dataset contributes to the preservation and promotion of the Chakma language by making linguistic data available for analysis and development of language-related technologies. Data Sources: The dataset may have been compiled from various linguistic sources, native speakers, or linguistic experts with expertise in the Chakma language. Dataset Size: The Chakma Language POS Tagging Dataset comprises a total of 1156*4 data points, providing a substantial corpus of Chakma text for linguistic analysis and NLP research.
DOI: 10.17632/gc233nkjgk.2
title: Chakma Language POS Tagging Dataset

description: The Chakma Language POS Tagging Dataset is a valuable linguistic resource designed for the analysis and understanding of the Chakma language. Chakma is a member of the Indo-Aryan language family and is primarily spoken by the Chakma people in the Chittagong Hill Tracts region of Bangladesh and in parts of India and Myanmar. This dataset aims to facilitate research and development in Chakma language processing, particularly in the domain of Part-of-Speech (POS) tagging.

Bengali: This column contains sentences and phrases in the Bengali script. Bengali is used for representing Chakma text in this dataset.

Chakma (Character): In this column, Chakma words or characters are presented in their native script. Chakma script is an abugida script used for writing the Chakma language.

Bengali (Chakma): This column provides a transliteration of Chakma words or characters into the Bengali script. It enables users who are familiar with Bengali to understand and work with the Chakma text more easily.

Parts of Speech (POS): The Parts of Speech column contains POS tags assigned to each word or character in the Chakma language. POS tagging is a crucial linguistic task that assigns grammatical categories (e.g., noun, verb, adjective) to each word in a text, enabling syntactic and semantic analysis.

Usage:
Linguistic Analysis: Researchers and linguists can use this dataset for linguistic analysis, syntactic studies, and language documentation of the Chakma language.
Natural Language Processing (NLP): NLP practitioners can leverage this dataset to build POS tagging models for Chakma, aiding in machine translation, sentiment analysis, and other NLP tasks.
Language Preservation: This dataset contributes to the preservation and promotion of the Chakma language by making linguistic data available for analysis and development of language-related technologies.

Data Sources:
The dataset may have been compiled from various linguistic sources, native speakers, or linguistic experts with expertise in the Chakma language.

Dataset Size:
The Chakma Language POS Tagging Dataset comprises a total of 1156*4 data points, providing a substantial corpus of Chakma text for linguistic analysis and NLP research.
DOI: 10.17632/nwhkw6y546.1
title: Durian Samples

description: Data Samples for Multiple Classifications
DOI: 10.17632/jxnfmkkpj3.1
title: Topographic Number Dataset

description: 1) Total Number of Original Images: 1322
2) Resolution of image: 28x28
3) Image type: Gray scale image
4) Image data type: unsigned integer 8-bit
 5) Dataset Size (Augmented): 26440
6) File format: PNG file
DOI: 10.17632/b2hys3mkjw.1
title: 48 Sub-Saharan Africa (SSA) Countries Datasets on ICT diffusion, trade liberalization and inclusive growth

description: The data covers 48 SSA countries from 2005 to 2020, focusing on the relationship between ICT, trade openness, and inclusive growth. ICT and inclusive growth are measured by composite indices using PCA analysis, while trade openness is measured by net foreign trade in goods and services. The data also includes control variables such as FDI, inflation, employment vulnerability, and social protection, which are derived from the World Bank database.
DOI: 10.6084/m9.figshare.24794508
title: Sigma dissimilarity analyses based on current and projected climate change

description: Sigma dissimilarity analyses to estimate exposure of threatened and commercial species to future novel conditions in the European marine protected areas
DOI: 10.17632/xz9rx5wfc5.1
title: Tamil Palmleaf Dataset

description: Data for Pulication in Data in Brief -The name of the samples are [NALADIYAR - ’NALADIYAR ORIGINAL’ (27) ,'NALADIYAR BINARIZED' (26)], [THOLKAPPIYAM -‘THOLKAPPIYAM ORIGINAL’ (221), 'THOLKAPPIYAM BINARIZED' (163)] and [THIRIKADUGAM - ‘THIRIKADUGAM ORIGINAL ’ (14), 'THIRIKADUGAM BINARIZED'(10)']. Samples contain information on mortal discipline and health, authoritative text on Tamil grammar, and medicine.
DOI: 10.17632/ptby9525tj.1
title: Research Data for "The impacts of future sectoral change on the greenhouse gas emissions of construction materials for Swiss residential buildings"

description: This is the research data for jounral article "The impacts of future sectoral change on the greenhouse gas emissions of construction materials for Swiss residential buildings".
DOI: 10.25446/oxford.24786258.v1
title: Pandemic PACT Grant Tracker (raw)

description: Pandemic PACT Grant Tracking data (raw)................
DOI: 10.17632/x2yd4dt526.1
title: Code and data used for the JFE article "Disagreement, information quality and asset prices"

description: Matlab code and data used in the paper "Disagreement, information quality and asset prices", as well as instructions for replicating the results.
DOI: 10.17632/x2yd4dt526.2
title: Code and data used for the JFE article "Disagreement, information quality and asset prices"

description: Matlab code and data used in the paper "Disagreement, information quality and asset prices", as well as instructions for replicating the results.
DOI: 10.25446/oxford.24773787
title: Pandemic PACT data dictionary

description: Pandemic PACT data dictionary
DOI: 10.25446/oxford.24773787.v1
title: Pandemic PACT data dictionary

description: Pandemic PACT data dictionary
DOI: 10.25446/oxford.24763548
title: Pandemic PACT Grant Tracker (labelled)

description: Grant tracking data (labelled).........
DOI: 10.25446/oxford.24763548.v1
title: Pandemic PACT Grant Tracker (labelled)

description: Grant tracking data (labelled).........
DOI: 10.17632/f7rs6bzkbc.1
title: Synthetic FFER dataset

description: The Synthetic Fake Facial Emotion Recognition (FFER) dataset created by Stable Diffusion model. The dataset includes 7 emotion categories: angry, disgust, fear, happy, sad, surprise, and neutral, each with over 3000 synthetic facial images with 64x64 pixels.
DOI: 10.17632/f7rs6bzkbc.2
title: The Synthetic Fake Facial Emotion Recognition (FFER) dataset

description: The Synthetic Fake Facial Emotion Recognition (FFER) dataset created by Stable Diffusion model. The dataset includes 7 emotion categories: angry, disgust, fear, happy, sad, surprise, and neutral, each with over 3000 synthetic facial images with 64x64 pixels.
DOI: 10.6084/m9.figshare.24191140
title: New approach methodologies (NAM) to characterize neurotoxic chemicals for use in risk assessment: case study of four neurotoxic pesticides

description: Neurotoxic pesticides: Cyfluthrin (CYF), βCYF, endosulfan (ENDO) and chlorpyrifos-oxon (CPFO) were examined using new approach methodologies (NAM) to assess ToxCast <i>in vitro</i> neurotoxic assay activities (50% concentration–AC<sub>50</sub>s), chemical-target specificity (Z-Scores) and <i>in vitro</i> to <i>in vivo</i> extrapolation with ToxCast assay AC<sub>50</sub>s as inputs to calculate human Administered Equivalent Dose (AED<sub>Human</sub> mg/kg) through Integrated Chemical Environment (ICE) models. Fold differences (FD) between AED<sub>Human</sub> NAM predictions and adjusted (animal to human interspecies extrapolation) <i>in vivo</i> regulatory neurotoxicity points of departure (POD) were calculated to assess modeled predictions against measured <i>in vivo</i> data. Interspecies (animal to human) <i>in vivo</i> adjusted PODs were 0.147 mg/kg/d (CYF/βCYF), 0.180 mg/kg/d (ENDO) and 0.106 mg/kg/d (CPFO). ToxCast neurotoxicity assay results supported <i>in vivo</i> observations: CYF, βCYF and CPFO showed depressed neuroactivity, while ENDO showed increased neuroactivity. βCYF is known to have twice the toxicity of CYF <i>in vivo</i> but was less toxic in the <i>in vitro</i> assays. Percent assays with Z-Scores greater than 3 for CYF, βCYF, ENDO and CPFO were 100%, 100%, 67% and 71% respectively. Most AED<sub>Human</sub> were less than 10-FD indicating good predictivity of the <i>in vivo</i> adjusted PODs with ICE models. The NAMs were useful for characterizing the main neurotoxic activities and the ICE models were highly predictive of <i>in vivo</i> PODs to support mechanisms in risk assessment.
DOI: 10.6084/m9.figshare.24191140.v1
title: New approach methodologies (NAM) to characterize neurotoxic chemicals for use in risk assessment: case study of four neurotoxic pesticides

description: Neurotoxic pesticides: Cyfluthrin (CYF), βCYF, endosulfan (ENDO) and chlorpyrifos-oxon (CPFO) were examined using new approach methodologies (NAM) to assess ToxCast <i>in vitro</i> neurotoxic assay activities (50% concentration–AC<sub>50</sub>s), chemical-target specificity (Z-Scores) and <i>in vitro</i> to <i>in vivo</i> extrapolation with ToxCast assay AC<sub>50</sub>s as inputs to calculate human Administered Equivalent Dose (AED<sub>Human</sub> mg/kg) through Integrated Chemical Environment (ICE) models. Fold differences (FD) between AED<sub>Human</sub> NAM predictions and adjusted (animal to human interspecies extrapolation) <i>in vivo</i> regulatory neurotoxicity points of departure (POD) were calculated to assess modeled predictions against measured <i>in vivo</i> data. Interspecies (animal to human) <i>in vivo</i> adjusted PODs were 0.147 mg/kg/d (CYF/βCYF), 0.180 mg/kg/d (ENDO) and 0.106 mg/kg/d (CPFO). ToxCast neurotoxicity assay results supported <i>in vivo</i> observations: CYF, βCYF and CPFO showed depressed neuroactivity, while ENDO showed increased neuroactivity. βCYF is known to have twice the toxicity of CYF <i>in vivo</i> but was less toxic in the <i>in vitro</i> assays. Percent assays with Z-Scores greater than 3 for CYF, βCYF, ENDO and CPFO were 100%, 100%, 67% and 71% respectively. Most AED<sub>Human</sub> were less than 10-FD indicating good predictivity of the <i>in vivo</i> adjusted PODs with ICE models. The NAMs were useful for characterizing the main neurotoxic activities and the ICE models were highly predictive of <i>in vivo</i> PODs to support mechanisms in risk assessment.
DOI: 10.6084/m9.figshare.24377258
title: Concentrations, sources, and health risks of potentially toxic elements in milled maize

description: Milling process is an essential step in food processing, which often determines the quality of food accessible to consumers. The contributions of dry and wet milling operations to contamination levels, sources, and health risks of potentially toxic elements (PTEs) in processed maize were evaluated in this study. Maize samples procured from a major market in Ilorin were milled and analyzed for 8 PTEs (Cd, Fe, Pb, As, Mn, Cu, Zn, and Cr) using Atomic Absorption Spectrophotometer. Apart from Cd, Pb, and As, the mean concentrations of other analyzed PTEs were within the permissible limits for PTEs in foodstuffs set by Codex Alimentarius Commission. The mean concentrations of Pb, Mn, and Zn increased from 2.75, 0.78, and 0.46 mg/kg in maize control to 3.03, 0.80, and 0.65 mg/kg in dry-milled maize, respectively, while mean concentrations of Fe, Pb, and Zn increased from 1.33, 2.75, and 0.46 mg/kg in maize control to 1.41, 2.90, and 0.49 mg/kg in wet-milled maize, respectively. The probable PTEs sources in milled maize identified using the positive matrix factorization model include wear of grinding disks; emissions from leaded fuel combustion; lubrication oil leakage during milling; vehicle traffic emissions; application of pesticides and herbicides; use of manure and Zn-enriched fertilizer; and irrigation with industrial wastewater. Assessment of hazard indexes (HIs) and lifetime cancer risks (LCR) revealed that associated health risks of PTEs from consumption of milled maize were insignificant, although HI and LCR results suggest the possibilities of health deterioration in children and adults from long-term consumption of milled maize in Ilorin. This study advocates improvement in maize cultivation and milling processes to avert PTEs contamination.
DOI: 10.6084/m9.figshare.24377258.v1
title: Concentrations, sources, and health risks of potentially toxic elements in milled maize

description: Milling process is an essential step in food processing, which often determines the quality of food accessible to consumers. The contributions of dry and wet milling operations to contamination levels, sources, and health risks of potentially toxic elements (PTEs) in processed maize were evaluated in this study. Maize samples procured from a major market in Ilorin were milled and analyzed for 8 PTEs (Cd, Fe, Pb, As, Mn, Cu, Zn, and Cr) using Atomic Absorption Spectrophotometer. Apart from Cd, Pb, and As, the mean concentrations of other analyzed PTEs were within the permissible limits for PTEs in foodstuffs set by Codex Alimentarius Commission. The mean concentrations of Pb, Mn, and Zn increased from 2.75, 0.78, and 0.46 mg/kg in maize control to 3.03, 0.80, and 0.65 mg/kg in dry-milled maize, respectively, while mean concentrations of Fe, Pb, and Zn increased from 1.33, 2.75, and 0.46 mg/kg in maize control to 1.41, 2.90, and 0.49 mg/kg in wet-milled maize, respectively. The probable PTEs sources in milled maize identified using the positive matrix factorization model include wear of grinding disks; emissions from leaded fuel combustion; lubrication oil leakage during milling; vehicle traffic emissions; application of pesticides and herbicides; use of manure and Zn-enriched fertilizer; and irrigation with industrial wastewater. Assessment of hazard indexes (HIs) and lifetime cancer risks (LCR) revealed that associated health risks of PTEs from consumption of milled maize were insignificant, although HI and LCR results suggest the possibilities of health deterioration in children and adults from long-term consumption of milled maize in Ilorin. This study advocates improvement in maize cultivation and milling processes to avert PTEs contamination.