cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/2sm93h8t7y.2
title: DATASET FOR PERFORMANCE OF SUPERIOR DAIRY CATTLE SIRES

description: This study's object includes animal identity data, pedigree data, and milk production performance records for Holstein Friesian dairy cows born between 2017-2021. This study's parameters included milk yield, length of lactation, peak production, and dry length period. Another factor to consider is the environmental state. High air temperatures have a negative impact on dairy cow welfare and production across Europe, especially tropical regions. This causes severe cattle losses. Humidity, air velocity, and insolation all have a major impact on animal thermoregulation. Special indices are employed to measure the overall impact of various environmental conditions on animals. The temperature-humidity index (THI) is commonly used to assess how ambient temperature and relative humidity affect animals. Genetic advancement on selection indicators in dairy farming by 90-95% depends on the amount of breeding value of bull breeders. Realizing how breeding value (BV) is passed down across generations is crucial for selecting the right pairs and advancing the breed. Accurate and detailed forecasts help farmers decrease economic risks when selecting genetic material. Estimation of breeding value uses the BLUP method since this method is the most accurate estimated breeding value of sire compared to other methods The production performance of FH dairy cattle is included in the good category. For 1st lactation, which has an average milk production of 8,029.28 ± 1,112 kg; length of lactation 321.26 ± 38.48 days; peak production on day 85.36 ± 29.25 with milk production of 32.55 ± 4.16 kg; and the dry length period was 51.37 ± 9.33 days. Lactation 2 milk production 7,761.66 ± 1,145 kg; length of lactation 323.66 ± 43.06 days; peak production on day 58.43 ± 21.11 with milk production 40.79 ± 5.30 kg; and dry length period was 65.10 ± 22.69 days. Lactation 3 milk production 7,788.92 ± 1,148 kg; length of lactation 326.64 ± 46.74 days; peak production on day 61.88 ± 22.72 with milk production 43.62 ± 5.11 kg; and the dry length period was 65.00 ± 20.49 days. Lactation 4 milk production was 7,484.18 ± 1,133 kg, lactation length was 323.04 ± 42.23 days, peak production was on day 66.39 ± 24.26, milk production was 43.82 ± 5.68, and the dry length period was 65.78 ± 21.60 days. The heritability value of FH cow milk production namely 0.03 ± 0.02, is included in the low category. Differences in heritability values is due to differences in the type of data and statistical analysis used. Low heritability estimates indicated that the differences in the performance traits of Holstein cows were due to different nutritional, climatic conditions, and management practices prevalent over different times. Ranking of 10 sire’s that have the genetic potential of their offspring based on their estimated breeding value, namely O.S. Elmer-XA, M.Z. Merlin-XA, L. Muscadet-XA, C.Toyjet, S. Gypsy B, WestCoastPldge, M.Z. Merlin-ET, Brasilia, L.JetBowser-XA, and MRMUDD-XA.
DOI: 10.17632/66mckd8dby.1
title: Organo-mineral lithification data

description: Research Data include EDS, XRD, FTIR, Raman spectra, XPS, TOF-SIMS, EELS, and DFT data associated with this article.
DOI: 10.17632/735f394fd4.1
title: Construction of circRNA-miRNA-mRNA Network Reveal Functional circRNAs and Key Genes in Acute Spinal Cord Injury.

description: The raw data of Western Blot.
DOI: 10.17632/n8zy62zc23.1
title: Research Data

description: Nickel isotopic composition of sulfides from Jinchuan deposit
DOI: 10.17632/23n5xfjhyt.1
title: Reference Dataset for Text Mining Type 2 Diabetes Candidate Genes

description: The present disease-gene association data contains evidence or reference sentences which contain this disease-gene association information, which is further classified into 4 classes: Yes, No, Ambiguous and X each pertaining to Positive, Negative, Ambiguous and Not related disease-gene associations respectively. This data serves as reference data for the training text mining-based biological literature classifiers which can be used to predict classes of published literature, not just for Type 2 diabetes, but can also be expanded beyond to encompass a wide range of disease and their complications. The compilation of positively associated genes derived from these predictions can then be utilized for in-depth system-level analysis of T2D.
DOI: 10.17632/mpgzxmfhrc.1
title: Model: ML confusion matrix for Isolation

description: Purpose:

Simulated dataset for exploring the performance of a machine learning model in classifying individuals as "at risk" or "not at risk" based on a set of features.
Used for testing model accuracy, evaluating feature importance, and understanding model behavior under varying conditions.
Key Components:

Features:
Synthetic, not based on real-world data.
5 features:
Question1, Question2, Question3 (random integers between 1 and 6)
Trait1, Trait2 (randomly generated numbers)
Represent potential questionnaire responses or other relevant attributes.
Target Variable:
Binary (0 for "not at risk", 1 for "at risk")
Simulated with a 70/30 class distribution (30% "not at risk", 70% "at risk")
Sample Size:
2000 samples
Machine Learning Model:
Random Forest Classifier with 100 estimators and a maximum depth of 10
Trained and evaluated using standard metrics (accuracy, classification report, confusion matrix)
Considerations:

Simulated Data: Does not reflect the complexity and nuances of real-world data.
Feature Meaning: Actual meanings of features are not specified, limiting interpretation of results.
Class Balance: Adjusted to be more balanced, but still not representative of all real-world scenarios.
Next Steps:

Validate with Real-World Data: Assess model performance on actual data to ensure generalisability.
Incorporate Additional Features: Explore incorporating more complex and realistic features.
Explore Different Models: Experiment with other algorithms to compare performance.
Address Class Imbalance: Consider techniques like oversampling or under-sampling to handle imbalanced datasets effectively.
DOI: 10.17632/9b5jhshbgk.1
title: Dataset for: Clinical significance of neuronal autoantibodies in patients with immune checkpoint inhibitor-induced neurological immune related adverse events 

description: Dataset for: Clinical significance of neuronal autoantibodies in patients with immune checkpoint inhibitor-induced neurological immune related adverse events
DOI: 10.17632/9b5jhshbgk.2
title: Dataset for: Clinical significance of neuronal autoantibodies in patients with immune checkpoint inhibitor-induced neurological immune related adverse events 

description: Dataset for: Clinical significance of neuronal autoantibodies in patients with immune checkpoint inhibitor-induced neurological immune related adverse events
DOI: 10.17632/9b5jhshbgk.3
title: Dataset for: Autoantibody profiles in patients with immune checkpoint inhibitor-induced neurological immune related adverse events. 

description: Dataset for: Autoantibody profiles in patients with immune checkpoint inhibitor-induced neurological immune related adverse events.
DOI: 10.17632/9b5jhshbgk.4
title: Dataset for: Autoantibody profiles in patients with immune checkpoint inhibitor-induced neurological immune related adverse events. 

description: Dataset for: Autoantibody profiles in patients with immune checkpoint inhibitor-induced neurological immune related adverse events.
DOI: 10.17632/9b5jhshbgk.5
title: Dataset for: Autoantibody profiles in patients with immune checkpoint inhibitor-induced neurological immune-related adverse events.

description: Dataset for: Autoantibody profiles in patients with immune checkpoint inhibitor-induced neurological immune-related adverse events.
DOI: 10.17632/z6f6r7kysn.1
title: Data of breakdown CVTs from the experimental platform

description: There are 12 files corresponding to the 12 cases listed in Table 4, and each file is named in accordance with the specified case. In each file, there is a 60-row and 7-column matrix indicating 60 samples from 7 simulators.  The value of each element is the amplitude of the secondary voltage with a nominal value of 4 Volts.
DOI: 10.17632/zkzczd5w26.1
title: Eutrophication Data

description: The dataset provides a detailed overview of various environmental parameters measured in Thol Lake, India, relevant to understanding eutrophication. It includes data on nutrient concentrations such as ammonia (NH3 & NH4+), nitrates (NO3), and total soluble phosphorus, which are critical indicators of potential algal blooms and eutrophication. Carbon dioxide (CO2) and dissolved oxygen levels are also recorded, offering insights into the respiratory and photosynthetic activities within the lake. The dataset encompasses measurements of total suspended solids (TSS), indicating particulate matter levels, along with sedimentation rates (both labile and refractory detrital sediment). Additionally, phytoplankton metrics (chlorophyll, biomass), water volume, temperature, wind speed, light intensity, pH levels, and Secchi depth provide a comprehensive view of the lake's ecological condition. These measurements, combined with the temporal and spatial data obtained from Sentinel-2 satellite imagery and ERA 5 meteorological data, create a multi-faceted picture of Thol Lake's eutrophication process, capturing both the immediate water quality parameters and the broader environmental factors influencing the lake's ecosystem.
DOI: 10.17632/z6f6r7kysn.2
title: Data of breakdown CVTs from the experimental platform

description: There are 12 files corresponding to the 12 cases listed in Table 6, and each file is named following the specified case. Each file has a 60-row and 7-column matrix, which indicates 60 samples from 7 simulators. The value of each element is the amplitude of the secondary voltage with a nominal value of 4 Volts.
DOI: 10.17632/gfghhfvs2x.1
title: Enhancing aquaculture sustainability and profitability: Effects of black soldier fly larval meal on the performance of African Catfish (Clarias gariepinus) in Cameroon

description: The integration of sustainable sources of protein is imperative for economically profitable and ecologically friendly aquaculture. In this study, we investigated the potential of insect-based protein in the diet of African catfish as an alternative to fishmeal. The insect-based protein was obtained from black soldier fly larvae meals (BSFLM) obtained using different larvae killing methods was incorporated into the local catfish diet at 0%, BSFLM0; 50%, BSFLM50; 75%, BSFLM75; and 100%, BSFLM100. A standard commercial diet was used as a control and the catfish were fed for an experimental period of 120 days. Each diet was repeated three times and their nutritional value was assessed using growth parameters, sustainability of the system and fish flesh quality. No adverse effect was recorded on fish flesh quality. We concluded that fishmeal could be replaced with BSFLM up to 75% without adverse effect on growth performance, survival, and taste of catfish but reduce the cost of production and increase fish farming sustainability.
DOI: 10.21942/uva.24949077.v4
title: Yet Another Classifier

description: Long Chipwhisperer measurement
DOI: 10.6084/m9.figshare.25039382
title: ferrinetal_Interactiveeffectsofwarmingabandnanoonthegutmicrobiomeofsoilcollembola

description: Nanoplastics and antibiotics are among the most abundant chemical pollutants of soils, but their interplay with global warming remains poorly understood. Finding ways to better assess the impact of these xenobiotics in soils is mandatory to protect the biodiversity of soil organisms and ecosystem services. The springtail<i> Folsomia candida</i> (Class Collembola) is a standard model for ecotoxicological assays with potential as a bioindicator of soil xenobiotics. Little is known, however, about their gut microbiome and how it might respond to warming and these pollutants. We exposed populations of <i>F. candida</i> in microcosms to either nanoplastics or an antibiotic under two temperatures. The antibiotic treatment consisted of colistin addition (50 g·kg<sup>-1 </sup>of dry soil), and the nanoplastic treatment consisted of polystyrene particles 0.044 µm in diameter (0.1 g·kg<sup>-1 </sup>of dry soil). Both treatments were incubated at 20 and 22 °C for two months, and their bacterial gut microbiomes were then sequenced. Warming strongly interacted with the impact of both xenobiotics on <i>F. candida</i> gut microbiome. Exposure to nanoplastics at 20 °C decreased the abundance of the most dominant bacterial phyla and families, and decreased the evenness of the gut microbiome. Warming of +2 °C, however, increased the abundances and evenness of the dominant families. Surprisingly, Gram-negative bacteria targeted by colistin were not globally affected. And at genus-level, the endosymbiont <i>Wolbachia </i>controlled the compositional shifts under nanoplastic addition, potentially driving the response of the gut microbiome to the particles. Our results also indicated that warming was a major driver that modulated the impacts of the antibiotic and nanoplastics. We illustrate how the gut microbiomes of springtails are sensitive communities responsive to xenobiotics and provide evidence of the need to combine multiple factors of global change operating simultaneously if we are to understand the responses of communities of soil arthropods and their microbiomes.
DOI: 10.6084/m9.figshare.25039382.v1
title: ferrinetal_Interactiveeffectsofwarmingabandnanoonthegutmicrobiomeofsoilcollembola

description: Nanoplastics and antibiotics are among the most abundant chemical pollutants of soils, but their interplay with global warming remains poorly understood. Finding ways to better assess the impact of these xenobiotics in soils is mandatory to protect the biodiversity of soil organisms and ecosystem services. The springtail<i> Folsomia candida</i> (Class Collembola) is a standard model for ecotoxicological assays with potential as a bioindicator of soil xenobiotics. Little is known, however, about their gut microbiome and how it might respond to warming and these pollutants. We exposed populations of <i>F. candida</i> in microcosms to either nanoplastics or an antibiotic under two temperatures. The antibiotic treatment consisted of colistin addition (50 g·kg<sup>-1 </sup>of dry soil), and the nanoplastic treatment consisted of polystyrene particles 0.044 µm in diameter (0.1 g·kg<sup>-1 </sup>of dry soil). Both treatments were incubated at 20 and 22 °C for two months, and their bacterial gut microbiomes were then sequenced. Warming strongly interacted with the impact of both xenobiotics on <i>F. candida</i> gut microbiome. Exposure to nanoplastics at 20 °C decreased the abundance of the most dominant bacterial phyla and families, and decreased the evenness of the gut microbiome. Warming of +2 °C, however, increased the abundances and evenness of the dominant families. Surprisingly, Gram-negative bacteria targeted by colistin were not globally affected. And at genus-level, the endosymbiont <i>Wolbachia </i>controlled the compositional shifts under nanoplastic addition, potentially driving the response of the gut microbiome to the particles. Our results also indicated that warming was a major driver that modulated the impacts of the antibiotic and nanoplastics. We illustrate how the gut microbiomes of springtails are sensitive communities responsive to xenobiotics and provide evidence of the need to combine multiple factors of global change operating simultaneously if we are to understand the responses of communities of soil arthropods and their microbiomes.
DOI: 10.17632/cydwhtbp3k.1
title: End-to-End datasets with Aug

description: This dataset is designed for mobile robot navigation and was built to test a navigation prototype using only a single camera. The dataset contains 4 classes: forward, left, right, and stop with Aug
DOI: 10.17632/545wb2gnd7.2
title: Associative Memory, Beliefs and Market Interactions

description: The files allow to replicate the results presented in our paper "Associative Memory, Beliefs and Market Interactions".

The second zip file adds a readme file to the STATA do-file and STATA datasets.
DOI: 10.17632/sn9hcj8mjh.1
title: Research on leader-follower product sales competition based on market access restrictions and forward contract considerations

description: The program code of "research on leader-follower product sales competition based on market access restrictions and forward contract considerations".
DOI: 10.17632/svr3fzk2nt.1
title: EmoAds: A Comprehensive Audio Dataset for Emotional Context Analysis in Advertisements

description: The dataset is a valuable resource that incorporates textual and audio data, facilitating the recognition of emotions and evaluation of advertisement effectiveness. It has the potential to resolve concerns regarding the impact of advertisements on the emotions and purchase intent of users. The data collection procedure involved a survey for real-world user feedback on advertisements, yielding a textual dataset containing statements relating to emotions. Additionally, the dataset contains 100 audio advertisements in WAV format that were initially segmented into 60-second segments. The result of further division was 1,000 audio snippets. The dataset was enriched by various audio feature extraction techniques, including MFCC, Short Time Energy, Zero Crossing Rate, Power Spectral Density, and Spectrogram Analysis. The survey's dependent variables are Arousal (A), Valence (V), Dominance (D), Liking (L), and Purchase (P). Independent features are coefficient values extracted from audio feature extraction techniques. The dataset incorporates these characteristics for thorough analysis. Generalization simplified the use of classification and prediction algorithms by reducing the rating scale for emotional statements from 1 to 5 to 1 to 3, thereby reducing their complexity. The potential of the dataset includes emotion recognition, advertisement evaluation, algorithm benchmarking, and advertising strategy optimization. Companies, marketers, and advertisers can improve their advertising strategies and comprehend public perception and purchase rates. It also functions as a standard for testing algorithms. There may be correlations between audio features and user-reported emotions or purchase intent that are noteworthy. These findings can be utilized to create more effective advertisements and content.
DOI: 10.17632/svr3fzk2nt.2
title: EmoAds: A Comprehensive Audio Dataset for Emotional Context Analysis in Advertisements

description: The dataset is a valuable resource that incorporates textual and audio data, facilitating the recognition of emotions and evaluation of advertisement effectiveness. It has the potential to resolve concerns regarding the impact of advertisements on the emotions and purchase intent of users. The data collection procedure involved a survey for real-world user feedback on advertisements, yielding a textual dataset containing statements relating to emotions. Additionally, the dataset contains 100 audio advertisements in WAV format that were initially segmented into 60-second segments. The result of further division was 1,000 audio snippets. The dataset was enriched by various audio feature extraction techniques, including MFCC, Short Time Energy, Zero Crossing Rate, Power Spectral Density, and Spectrogram Analysis. The survey's dependent variables are Arousal (A), Valence (V), Dominance (D), Liking (L), and Purchase (P). Independent features are coefficient values extracted from audio feature extraction techniques. The dataset incorporates these characteristics for thorough analysis. Generalization simplified the use of classification and prediction algorithms by reducing the rating scale for emotional statements from 1 to 5 to 1 to 3, thereby reducing their complexity. The potential of the dataset includes emotion recognition, advertisement evaluation, algorithm benchmarking, and advertising strategy optimization. Companies, marketers, and advertisers can improve their advertising strategies and comprehend public perception and purchase rates. It also functions as a standard for testing algorithms. There may be correlations between audio features and user-reported emotions or purchase intent that are noteworthy. These findings can be utilized to create more effective advertisements and content.
DOI: 10.17632/svr3fzk2nt.3
title: EmoAds: A Comprehensive Audio Dataset for Emotional Context Analysis in Advertisements

description: The dataset is a valuable resource that incorporates textual and audio data, facilitating the recognition of emotions and evaluation of advertisement effectiveness. It has the potential to resolve concerns regarding the impact of advertisements on the emotions and purchase intent of users.

The data collection procedure involved a survey for real-world user feedback on advertisements, yielding a textual dataset containing statements relating to emotions. Additionally, the dataset contains 100 audio advertisements in WAV format that were initially segmented into 60-second segments. The result of further division was 1,000 audio snippets. The dataset was enriched by various audio feature extraction techniques, including MFCC, Short Time Energy, Zero Crossing Rate, Power Spectral Density, and Spectrogram Analysis.

The survey's dependent variables are Arousal (A), Valence (V), Dominance (D), Liking (L), and Purchase (P). Independent features are coefficient values extracted from audio feature extraction techniques. The dataset incorporates these characteristics for thorough analysis. Generalization simplified the use of classification and prediction algorithms by reducing the rating scale for emotional statements from 1 to 5 to 1 to 3, thereby reducing their complexity.

The potential of the dataset includes emotion recognition, advertisement evaluation, algorithm benchmarking, and advertising strategy optimization. Companies, marketers, and advertisers can improve their advertising strategies and comprehend public perception and purchase rates. It also functions as a standard for testing algorithms. There may be correlations between audio features and user-reported emotions or purchase intent that are noteworthy. These findings can be utilized to create more effective advertisements and content.
DOI: 10.17632/fdvcpx6vc3.1
title: Supplemental: Timing of cutaneous immune-related adverse events impacts survival in cancer patients 3 treated with immune-checkpoint inhibitors: A multi-institutional cohort study

description: Supplemental for Timing of cutaneous immune-related adverse events impacts survival in cancer patients 3 treated with immune-checkpoint inhibitors: A multi-institutional cohort study
DOI: 10.17632/74hpnb7cp7.1
title: Effect of antiepileptic drug monotherapy on endogenous sex hormonal profile in men and women with epilepsy.

description: Supporting information for the systematic review and meta-analysis conducted for elucidating the effect antiepileptic drug monotherapy on endogenous sex hormonal profile in men and women with epilepsy.
DOI: 10.17632/c3tw84hys5.1
title: RSL Online Focus Group

description: El conjunto de datos que presentamos aporta los resultados de investigación del artículo "Focus Group online en la investigación social: revisión de la literatura e implementación en contextos de Aprendizaje-Servicio virtual". Este dataset incluye una selección de artículos de investigación producidos por la Revisión Sistemática de la Literatura, empleada como metodología principal del artículo, en formato RIS (Research Information Systems), acompañados de resúmenes ampliados de cada uno de ellos.

Además de los artículos y resúmenes, proporcionamos códigos y herramientas que permiten generar nubes de palabras y otros gráficos relacionados con el contenido de los artículos.
DOI: 10.17632/92r8jgbb9x.1
title: Municipal solid waste generation forecasting

description: Supplementary materials for reproducibility of the study on the use of socio-economic, climatic, and land use land cover patterns for municipal solid waste generation forecasting.