cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/tp2r6v9ncv.1
title: Effect of dicarboxylic acids (Photon) on the metabolite and antioxidant enzymes of Z. mays

description: The results indicate that Photon revealed significant changes in various metabolites. On the other hand, it had no significant priming effect on antioxidant enzymes.
DOI: 10.17632/x9fmz9zcth.1
title: Fuente de datos: revisión sistemática PRISMA sobre el impacto y calidad de revistas académicas de ciencias de la educación en idioma gallego (período 2019-2023).

description: Esta es una publicación de los datos correspondientes a la «Revisión sistemática PRISMA sobre el impacto y calidad de revistas académicas de ciencias de la educación en idioma gallego: período 2019-2023» en el marco del Trabajo de Fin de Máster para la obtención del título de Máster Universitario en Investigación en Educación por la Universidad Internacional de Valencia. Tutor ha sido el Prof. Dr. Juan Carlos Fernández Rodríguez.

Objetivo: el presente trabajo de fin de máster que se titula «Revisión sistemática PRISMA sobre el impacto y calidad de revistas académicas de ciencias de la educación en idioma gallego: período 2019-2023» busca estudiar el impacto y calidad de la producción científica en idioma gallego del último lustro a través de las revistas académicas de las universidades públicas de Galicia. Diseño: se trata de una revisión sistemática bajo la metodología PRISMA. Métodos: se analizaron las revistas académicas de los servicios de publicación de las tres universidades públicas gallegas Universidade da Coruña, Universidade de Santiago de Compostela y Universidade de Vigo. Seleccionando las 9 pertenecientes al campo de ciencias de la educación. Resultados: Existe una producción de 47 artículos en gallego. La mayoría de las revistas indexan en al menos 8 bases de datos. Cuatro de las revistas no admiten artículos en gallego. Conclusiones: Es necesario aumentar la visibilidad de la producción existente, así como promover la producción en el idioma propio.

Palabras claves:Investigación, Publicación científica periódica, Derechos lingüísticos
DOI: 10.17632/xfk7w95kf2.1
title: Genetically proxied IL4/IL-13 inhibition was not associate with the risk of cutaneous T-cell lymphoma: Drug-Target Mendelian Randomization Analyses

description: This study aimed to delve into the potential causal nexus between dupilumab and cutaneous T-cell lymphomas (CTCL), leveraging publicly accessible, anonymized summary data sourced from prior genome-wide association studies (GWASs). To achieve this objective, we executed a drug target Mendelian randomization (MR) analysis, a rigorous methodology for inferring causality from observational data.
DOI: 10.6084/m9.figshare.25323205
title: FTIR

description: FTIR data of soils, precipitated and powdered CaCO3, sucrose, IHSS organic matter standards, ash, and south-west Western Australia vegetation.For all files, column A is Wavenumber (cm-1) and column B is Absorbance.
DOI: 10.15468/dl.zwpgf7
title: Occurrence Download

description: A dataset containing 33 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pristiophorus schroederi Springer & Bullis, 1960"
 ]
}

 The dataset includes 33 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0105472-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/sdhjyzkxsd.1
title: Lip Reading Vowel-Bangla (LRV-B) Dataset

description: The Lip Reading Vowel-Bangla (LRV-B) Dataset is a curated collection of video recordings focused on the articulation of Bangla vowels. It is designed to support research in lip reading and visual speech recognition for the Bangla language. The dataset captures the lip movements associated with 6 (six) key Bangla vowel sounds, providing a valuable resource for developing and evaluating models in speech recognition, language processing, and assistive technologies. By concentrating on vowels, the LRV-B Dataset addresses the specific challenges of recognizing and interpreting these fundamental speech components in Bangla.
DOI: 10.15468/dl.89m5ts
title: Occurrence Download

description: A dataset containing 249076 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is North America",
 "SpeciesKey is Sturnella neglecta Audubon, 1844",
 "HasCoordinate is TRUE",
 "Year is 2023"
 ]
}

 The dataset includes 249076 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0105454-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/jj49f22kw8.1
title: IIF-IgG4 for pemphigoid and pemphigus diseases

description: Indirect immunofluorescence with IgG4 for pemphigoid and pemphigus diseases
DOI: 10.17632/m43spdth8p.1
title: Supplementary Methods for "Risk of subsequent primary malignancies in patients with cutaneous leiomyosarcoma: a population-based analysis"

description: Supplementary Methods for "Risk of subsequent primary malignancies in patients with cutaneous leiomyosarcoma: a population-based analysis"
DOI: 10.17632/rcm9pm8749.1
title: EarwaxVOCsExtractionOptimization_rawdata

description: HS/GC-MS raw data files (.mzXML extension) obtained in the optimization steps and repeatability study of the earwax VOCs analysis
DOI: 10.17632/myrbfg4ffm.1
title: Supplementary Table I for "Risk of subsequent primary malignancies in patients with cutaneous leiomyosarcoma: a population-based analysis"

description: Supplementary Table I for "Risk of subsequent primary malignancies in patients with cutaneous leiomyosarcoma: a population-based analysis"
DOI: 10.17632/nrpm8ydybr.1
title: Aldehydes alter TGF-b signaling and induce obesity and cancer

description: Obesity and fatty liver diseases—metabolic dysfunction-associated steatotic liver disease (MASLD) and
metabolic dysfunction-associated steatohepatitis (MASH)—affect over one-third of the global population
and are exacerbated in individuals with reduced functional aldehyde dehydrogenase 2 (ALDH2), observed
in approximately 560 million people. Current treatment to prevent disease progression to cancer remains
inadequate, requiring innovative approaches. We observe that Aldh2-/- and Aldh2-/-Sptbn1+/- mice
develop phenotypes of human metabolic syndrome (MetS) and MASH with accumulation of endogenous aldehydes
such as 4-hydroxynonenal (4-HNE). Mechanistic studies demonstrate aberrant transforming growth
factor b (TGF-b) signaling through 4-HNE modification of the SMAD3 adaptor SPTBN1 (b2-spectrin) to pro
fibrotic and pro-oncogenic phenotypes, which is restored to normal with small interfering RNA (siRNA) to
SPTBN1. Significantly, therapeutic inhibition of SPTBN1 blocks MASH and fibrosis in a human model and,
additionally, improves glucose handling in Aldh2-/- and Aldh2-/-Sptbn1+/- mice. This study identifies SPTBN1 as a
critical regulator of the functional phenotype of toxic aldehyde-induced MASH and a potential therapeutic target.
DOI: 10.17632/3rztrysnvp.1
title: Russian Drug Use Disorders, Drug Crimes, Drug Court, Drug Police Data for 2019

description: Official indicators, officially published:

- Population in the regions of Russia,
- Drug dependence incidence,
- Harmful drug use prevalence,
- Injecting drug users (IDU) Prevalence per 100000,
- Number of HIV positive IDUs,
- Percent of HIV positive among registred IDUs,
- Incidence of Injection drug use,
- Relative rate of patients who inject drugs (rate among incidence),
- Prevalence of people with substance use (drugs+alcohol),
- Drugs dependence prevalence (no alcohol),
- Harmful drug use prevalence,
- HIV Incidence overall (not speciifc to drug users),
- HIV prevalence overall,
- Number of multidisciplinary institutions with outpatient narcological departments (offices),
- Bed occupancy (days per year),
- The average length of stay of the patient in the bed,
- Duration of treatment (days per patient),
- The number of schoolchildren and students tested for drugs,
- The number of schoolchildren and students who refused to test,
- The number of schoolchildren and students who refused to test,
- The number of schoolchildren and students testing positive for drugs,
- The number of schoolchildren and students testing positive for drugs,
- The involvement of drug users in illicit trafficking 2018,
- The criminality of drug addiction (the effect of drug addiction on drugs situation) 2018,
- Proportion of humans of drug related crimes to all humans with crimes,
- Proportion of youth (under 18) who are convicted for drug use among all who are convicted for drug use,
- Prevalence of drug use based on social studies (percent in a cohort),
- Prevalence of drug users based on police data,
- Death from drug poisoning based on pathology report,
- Opioid dependese prevalence (registered),
- Cannabis dependence prevalence (registered),
- Opioid dependese incidence (registered),
- Cannabis dependence incidence (registered)

All variables was downloaded from the Official published sources and was available online on January 2022.
Collected for the study (JMIR Preprints 08/01/2024:56006)
DOI: https://doi.org/10.2196/preprints.56006
DOI: 10.17632/3wdjvxmvsp.5
title: A Dataset for Integrated Analysis of Satellite Remote Sensing, Weather Variables and Nelore Cattle Supplementation Data in the Midwest Region of Brazil

description: This is a dataset of an experiment performed in in the School Farm of the Federal University of Mato Grosso do Sul (UFMS). UFMS is in the Midwest region of Brazil (Mato Grosso do Sul State). The school farm is located at 20°26'37.7"S 54°50'58.5"W. The data was collected in a 16ha paddock containing 26 Nelore breed animals with brachiaria Decumbens forage. The dataset contains weight and supplementation data for each animal, Multispectral data, and environmental data for a period of one year (December, 2022 - October, 2023). 

Along the period, every 13-28 days, all 26 animals in the paddock got their weights registered, as well as their supplementation data (supplementation delivered (kg) per day, average daily weight gain, time of each supplementation, and data for how many times and for how long each animal went to the feeder). For each days-interval, dates (START_DATE and FINAL_DATE), period identification (PERIOD) and day range (AMOUNT_DAYS) were registered, as well as animal identification (ANIMAL), its weight at the start and end (START_WEIGHT and FINAL_WEIGHT), its average daily weight gain (ADG), average daily supplementation (ADS), time for each daily supplementation (SUP_TIME), total supplementation amount for the whole herd (SUP_TOTAL), total time the animal spent at the feeder (TOTAL_TIME), how many times the animal went to the feeder (TOTAL_ATTENDANCE) and total of different days the animal spent at the feeder (TOTAL_DAYS).

Specifically, a couple more specific filter were applied to attendance and days data in order to get more specific supplementation data for each animal. We split the data for specific ranges of time ((6-12h), (12-18h), (18-00h), (6-8h), (12-14h), (18-20h)) and for specific date ranges (spring (09/23-12/21), summer (12/22-03/20), autumn (03/23-06/21), winter (06/22-09/22))

Multispectral data was acquired looking at the paddock location. The data was collected using Google Earth Engine API, based on Sentinel-2 multispectral images. Twenty sentinel-2 bands (BAND1, BAND2, BAND3, BAND4, BAND5, BAND6, BAND7, BAND8A, BAND9, BAND11, and BAND12) were acquired, and eight spectral indices (NDVI_INDEX, NDWI_INDEX, EVI_INDEX, LAI_INDEX, DVI_INDEX, GCI_INDEX, GEMI_INDEX, and SAVI_INDEX) were calculated and integrated into the dataset. 

Weather data was also collected using the Open-Meteo weather API. The data acquired are Mean Temperature of the period (TEMP_AVG), Rain sum registered in the period (RAIN_SUM), Average daily rain registered during the period (RAIN_AVG), Solar Radiation sum and Average Solar Radiation registered during the period (RAD_SOL_SUM,RAD_SOL_AVG), Average evapotranspiration registered during the period (EVAPOT), Average Relative Humidity registered during the period (HUM_REL_AVG) and Average Atmospheric Pressure registered during the period (PRES_ATM_AVG).
DOI: 10.17632/3wdjvxmvsp.6
title: A Dataset on Nelore Cattle Supplementation in the Midwest Region of Brazil for Integrated Analysis of Supplementation, Satellite Remote Sensing, and Weather Variables

description: This is a dataset of an experiment performed in in the School Farm of the Federal University of Mato Grosso do Sul (UFMS). UFMS is in the Midwest region of Brazil (Mato Grosso do Sul State). The school farm is located at 20°26'37.7"S 54°50'58.5"W. The data was collected in a 16ha paddock containing 26 Nelore breed animals with brachiaria Decumbens forage. The dataset contains weight and supplementation data for each animal, Multispectral data, and environmental data for a period of one year (December, 2022 - October, 2023). 

Along the period, every 13-28 days, all 26 animals in the paddock got their weights registered, as well as their supplementation data (supplementation delivered (kg) per day, average daily weight gain, time of each supplementation, and data for how many times and for how long each animal went to the feeder). For each days-interval, dates (START_DATE and FINAL_DATE), period identification (PERIOD) and day range (AMOUNT_DAYS) were registered, as well as animal identification (ANIMAL), its weight at the start and end (START_WEIGHT and FINAL_WEIGHT), its average daily weight gain (ADG), average daily supplementation (ADS), time for each daily supplementation (SUP_TIME), total supplementation amount for the whole herd (SUP_TOTAL), total time the animal spent at the feeder (TOTAL_TIME), how many times the animal went to the feeder (TOTAL_ATTENDANCE) and total of different days the animal spent at the feeder (TOTAL_DAYS).

Specifically, a couple more specific filter were applied to attendance and days data in order to get more specific supplementation data for each animal. We split the data for specific ranges of time ((6-12h), (12-18h), (18-00h), (6-8h), (12-14h), (18-20h)) and for specific date ranges (spring (09/23-12/21), summer (12/22-03/20), autumn (03/23-06/21), winter (06/22-09/22))

Multispectral data was acquired looking at the paddock location. The data was collected using Google Earth Engine API, based on Sentinel-2 multispectral images. Twenty sentinel-2 bands (BAND1, BAND2, BAND3, BAND4, BAND5, BAND6, BAND7, BAND8A, BAND9, BAND11, and BAND12) were acquired, and eight spectral indices (NDVI_INDEX, NDWI_INDEX, EVI_INDEX, LAI_INDEX, DVI_INDEX, GCI_INDEX, GEMI_INDEX, and SAVI_INDEX) were calculated and integrated into the dataset. 

Weather data was also collected using the Open-Meteo weather API. The data acquired are Mean Temperature of the period (TEMP_AVG), Rain sum registered in the period (RAIN_SUM), Average daily rain registered during the period (RAIN_AVG), Solar Radiation sum and Average Solar Radiation registered during the period (RAD_SOL_SUM,RAD_SOL_AVG), Average evapotranspiration registered during the period (EVAPOT), Average Relative Humidity registered during the period (HUM_REL_AVG) and Average Atmospheric Pressure registered during the period (PRES_ATM_AVG).
DOI: 10.17632/97n4pd3jcf.1
title: Supplemental Material for JAAD Article "A Scoping Review of Cost and Cost-Effectiveness Study Methods for Skin Cancer Treatments"

description: Supplemental Table 1. Search Terms to Identify Studies.
Supplemental Table 2. Data Sources of Included Studies.
Supplemental Figure 1. CONSORT Diagram.
DOI: 10.17632/3wdjvxmvsp.1
title: A Dataset for Integrated Analysis of Satellite Remote Sensing, Environmental Variables and Cattle Supplementation Data

description: This is a dataset from a 16ha paddock containing 20 Nelore breed animals with brachiaria Decumbens. The dataset contains weight and supplementation data for each animal and for a specific period of time (15 periods in total) as well as Hyperspectral data and environmental data for each period.

During 12 months, every 12-28 days, all 26 animals in the paddock got their weights registered, as well as their supplementation data (amount per day, average daily weight gain, time of each supplementation, and data for how many times and for how long each animal went to the feeder). For each period of days, the dates (START_DATE and FINAL_DATE) and day range (AMOUNT_DAYS) was registered, as well as the animal identification (ANIMAL), its weight at the start and end of the period (START_WEIGHT and FINAL_WEIGHT), its average daily weight gain (GMD), average daily supplementaion (SMD), time for each daily supplementation (SUP_TIME), total supplementation amount for the whole herd (SUP_TOTAL), total time the animal spent at the feeder (TOTAL_TIME), how many times the animal went to the feeder (TOTAL_ATTENDANCE) and total of different days the animal spent at the feeder (TOTAL_DAYS).

Specifically for the time, attendance and days data, a couple more specific filter were applied to them in order to get more specific supplementation data for each animal. We split the data for specific ranges of time ((6-12h), (12-18h), (18-00h), (6-8h), (12-14h), (18-20h)) and for specific date ranges (spring (09/23-12/21), summer (12/22-03/20), autumn (03/23-06/21), winter (06/22-09/22))

For the paddock, hyperspectral data was also acquired. The data was collected using Google Earth Engine API, based on Sentinel-2 hyperspectral images. Twenty sentinel-2 bands (B01, B02, B03, B04, B05, B06, B07, 8A, B09, B11, and B12) were acquired, and eight well-known spectral indices (NDVI, NDWI, EVI, LAI, DVI, GCI, GEMI, and SAVI) were calculated and integrated into the dataset. 

Environmental data was acquired using a weather API (Open-Meteo). The climate data acquired are Mean Temperature of the period (TEMP_AVG), Rain sum registered in the period (RAIN_SUM), Average daily rain registered during the period (RAIN_AVG), Solar Radiation sum and Average Solar Radiation registered during the period (RAD_SOL_SUM,RAD_SOL_AVG), Average evapotranspiration registered during the period (EVAPOT), Average Relative Humidity registered during the period (HUM_REL_AVG) and Average Atmospheric Pressure registered during the period (PRES_ATM_AVG).
DOI: 10.17632/3wdjvxmvsp.2
title: A Dataset for Integrated Analysis of Satellite Remote Sensing, Environmental Variables and Cattle Supplementation Data

description: This is a dataset from a 16ha paddock containing 20 Nelore breed animals with brachiaria Decumbens. The dataset contains weight and supplementation data for each animal and for a specific period of time (15 periods in total) as well as Hyperspectral data and environmental data for each period.

During 12 months, every 12-28 days, all 26 animals in the paddock got their weights registered, as well as their supplementation data (amount per day, average daily weight gain, time of each supplementation, and data for how many times and for how long each animal went to the feeder). For each period of days, the dates (START_DATE and FINAL_DATE) and day range (AMOUNT_DAYS) was registered, as well as the animal identification (ANIMAL), its weight at the start and end of the period (START_WEIGHT and FINAL_WEIGHT), its average daily weight gain (ADG), average daily supplementation (ADS), time for each daily supplementation (SUP_TIME), total supplementation amount for the whole herd (SUP_TOTAL), total time the animal spent at the feeder (TOTAL_TIME), how many times the animal went to the feeder (TOTAL_ATTENDANCE) and total of different days the animal spent at the feeder (TOTAL_DAYS).

Specifically for the time, attendance and days data, a couple more specific filter were applied to them in order to get more specific supplementation data for each animal. We split the data for specific ranges of time ((6-12h), (12-18h), (18-00h), (6-8h), (12-14h), (18-20h)) and for specific date ranges (spring (09/23-12/21), summer (12/22-03/20), autumn (03/23-06/21), winter (06/22-09/22))

For the paddock, hyperspectral data was also acquired. The data was collected using Google Earth Engine API, based on Sentinel-2 hyperspectral images. Twenty sentinel-2 bands (B01, B02, B03, B04, B05, B06, B07, 8A, B09, B11, and B12) were acquired, and eight well-known spectral indices (NDVI, NDWI, EVI, LAI, DVI, GCI, GEMI, and SAVI) were calculated and integrated into the dataset. 

Environmental data was acquired using a weather API (Open-Meteo). The climate data acquired are Mean Temperature of the period (TEMP_AVG), Rain sum registered in the period (RAIN_SUM), Average daily rain registered during the period (RAIN_AVG), Solar Radiation sum and Average Solar Radiation registered during the period (RAD_SOL_SUM,RAD_SOL_AVG), Average evapotranspiration registered during the period (EVAPOT), Average Relative Humidity registered during the period (HUM_REL_AVG) and Average Atmospheric Pressure registered during the period (PRES_ATM_AVG).
DOI: 10.17632/3wdjvxmvsp.3
title: A Dataset for Integrated Analysis of Satellite Remote Sensing, Environmental Variables and Cattle Supplementation Data

description: This is a dataset of an experiment performed in in the School Farm of the Federal University of Mato Grosso do Sul (UFMS). UFMS is in the Midwest region of Brazil (Mato Grosso do Sul State). The school farm is located at 20°26'37.7"S 54°50'58.5"W. The data were collected in a 16ha paddock containing 26 Nelore breed animals with brachiaria Decumbens forage. The dataset contains weight and supplementation data for each animal, Hyperspectral data, and environmental data for a period of one year (December, 2022 - November, 2023). 

Along the period, every 12-28 days, all 26 animals in the paddock got their weights registered, as well as their supplementation data (supplementation delivered (kg) per day, average daily weight gain, time of each supplementation, and data for how many times and for how long each animal went to the feeder). For each days-interval, dates (START_DATE and FINAL_DATE), period identification (PERIOD) and day range (AMOUNT_DAYS) were registered, as well as animal identification (ANIMAL), its weight at the start and end (START_WEIGHT and FINAL_WEIGHT), its average daily weight gain (ADG), average daily supplementation (ADS), time for each daily supplementation (SUP_TIME), total supplementation amount for the whole herd (SUP_TOTAL), total time the animal spent at the feeder (TOTAL_TIME), how many times the animal went to the feeder (TOTAL_ATTENDANCE) and total of different days the animal spent at the feeder (TOTAL_DAYS).

Specifically, a couple more specific filter were applied to attendance and days data in order to get more specific supplementation data for each animal. We split the data for specific ranges of time ((6-12h), (12-18h), (18-00h), (6-8h), (12-14h), (18-20h)) and for specific date ranges (spring (09/23-12/21), summer (12/22-03/20), autumn (03/23-06/21), winter (06/22-09/22))

Hyperspectral data was acquired looking at the paddock location. The data was collected using Google Earth Engine API, based on Sentinel-2 hyperspectral images. Twenty sentinel-2 bands (B01, B02, B03, B04, B05, B06, B07, 8A, B09, B11, and B12) were acquired, and eight spectral indices (NDVI, NDWI, EVI, LAI, DVI, GCI, GEMI, and SAVI) were calculated and integrated into the dataset. 

Environmental data was also collected using the Open-Meteo weather API. The data acquired are Mean Temperature of the period (TEMP_AVG), Rain sum registered in the period (RAIN_SUM), Average daily rain registered during the period (RAIN_AVG), Solar Radiation sum and Average Solar Radiation registered during the period (RAD_SOL_SUM,RAD_SOL_AVG), Average evapotranspiration registered during the period (EVAPOT), Average Relative Humidity registered during the period (HUM_REL_AVG) and Average Atmospheric Pressure registered during the period (PRES_ATM_AVG).