cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7851616
title: BOLA TARBIYASIDA OILANING ROLI VA AHAMIYATI

description: Oilada bolalarga juda kichik yoshdan boshlab barkamollik sifatlari tarbiyalanib boradi. Bola avvalo oila muhiti asosida tarbiyalanib, shakllanib boradi. Shuning uchun ham oilada bolaning har bir harakati, uning do’stlari, odamlarga muomalasi kabilar kuzatilib, undagi o’zgarishlar, xulqidagi kamchiliklar bartaraf etilib borishi lozim. Bolani yosh niholga o’xshatish mumkin. Yosh niholni o’stirishda uning ortiqcha shoxlarini kеsib, tinimsiz parvarishlab borsangiz u ko’rkam daraxtga aylanadi. Bola kichikligida juda shirin harakatlari va qiliqlari bilan ota-ona va atrofdagilar diqqatini jalb qiladi. Shunday davrda uning barcha injiqliklari va talablarini ota-ona bеkami ko’st bajaradi.
description: jamiyat , qarindosh urug`lar
DOI: 10.5281/zenodo.7851615
title: BOLA TARBIYASIDA OILANING ROLI VA AHAMIYATI

description: Oilada bolalarga juda kichik yoshdan boshlab barkamollik sifatlari tarbiyalanib boradi. Bola avvalo oila muhiti asosida tarbiyalanib, shakllanib boradi. Shuning uchun ham oilada bolaning har bir harakati, uning do’stlari, odamlarga muomalasi kabilar kuzatilib, undagi o’zgarishlar, xulqidagi kamchiliklar bartaraf etilib borishi lozim. Bolani yosh niholga o’xshatish mumkin. Yosh niholni o’stirishda uning ortiqcha shoxlarini kеsib, tinimsiz parvarishlab borsangiz u ko’rkam daraxtga aylanadi. Bola kichikligida juda shirin harakatlari va qiliqlari bilan ota-ona va atrofdagilar diqqatini jalb qiladi. Shunday davrda uning barcha injiqliklari va talablarini ota-ona bеkami ko’st bajaradi.
description: jamiyat , qarindosh urug`lar
DOI: 10.5281/zenodo.7934372
title: Results from correlation of asparagus area change to GDP growth

description: Results from the from correlation of asparagus area change to GDP growth as machine readable file.
DOI: 10.5281/zenodo.7934371
title: Results from correlation of asparagus area change to GDP growth

description: Results from the from correlation of asparagus area change to GDP growth as machine readable file.
DOI: 10.6084/m9.figshare.21750029
title: QADO: An RDF Representation of Question Answering Datasets and their Analyses for Improving Reproducibility

description: Measuring the quality of Question Answering (QA) systems is a crucial task to validate the results of novel approaches. However, there are already indicators of a reproducibility crisis as many published systems have used outdated datasets or use subsets of QA benchmarks, making it hard to compare results. We identified the following core problems: there is no standard data format, instead, proprietary data representations are used by the different partly inconsistent datasets; additionally, the characteristics of datasets are typically not reflected by the dataset maintainers nor by the system publishers. To overcome these problems, we established an ontology---Question Answering Dataset Ontology (QADO)---for representing the QA datasets in RDF. The following datasets were mapped into the ontology: the QALD series, LC-QuAD series, RuBQ series, ComplexWebQuestions, and Mintaka. Hence, the integrated data in QADO covers widely used datasets and multilinguality. Additionally, we did intensive analyses of the datasets to identify their characteristics to make it easier for researchers to identify specific research questions and to select well-defined subsets. The provided resource will enable the research community to improve the quality of their research and support the reproducibility of experiments. <br> Here, the mapping results of the QADO process, the SPARQL queries for data analytics, and the archived analytics results file are provided. <br> Up-to-date statistics can be created automatically by the script provided at the corresponding QADO GitHub RDFizer repository.
DOI: 10.6084/m9.figshare.21750029.v2
title: QADO: An RDF Representation of Question Answering Datasets and their Analyses for Improving Reproducibility

description: Measuring the quality of Question Answering (QA) systems is a crucial task to validate the results of novel approaches. However, there are already indicators of a reproducibility crisis as many published systems have used outdated datasets or use subsets of QA benchmarks, making it hard to compare results. We identified the following core problems: there is no standard data format, instead, proprietary data representations are used by the different datasets; additionally, we recognized a lack of understanding of which test questions are reused across different instances of a benchmark series; last but not least, the characteristics of datasets are typically not reflected by the dataset maintainers nor by the system publishers. To overcome these problems, we established an ontology---Question Answering Dataset Ontology (QADO)---for representing the QA datasets in RDF. The following datasets were mapped into the ontology: the QALD series, LC-QuAD series, RuBQ series, CWQ, and Mintaka. Hence, the integrated data in QADO covers widely used datasets and multilinguality. Additionally, we did intensive analyses of the datasets to identify their characteristics. The provided resource will enable the research community to improve the quality of their research and support the reproducibility of experiments. <br> Here, the mapping results of the QADO process, the SPARQL queries for data analytics, and archived analytics results file are provided.
DOI: 10.34740/kaggle/dsv/5682252
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.5281/zenodo.7934410
title: Sex-disaggregated Analysis of Risk Factors of COVID-19 Mortality Rates in India

description: This Zenodo resource contains the data used to perform analysis in the article "Sex-disaggregated Analysis of Risk Factors of COVID-19 Mortality Rates in India". Data The data is organized in the form of tables. hypothesis-test-data This table contains data used to perform the two tailed hypothesis test on gender mortality in different regions. <pre><code>* Region * Male_Deaths - Number of male COVID-19 deaths in region. * Female_Deaths - Number of female COVID-19 deaths in region. * Male_cases - Number of male COVID-19 positive in region. * Female_cases - Number of female COVID-19 positive in region. </code></pre> lasso-covid19India This table contains data used for analysis on cases throughout India. Columns from COVID-19 India data <pre><code>* State_Code * State * District * Confirmed * Active * Recovered * Deceased </code></pre> Columns taken from NFHS data <pre><code>* Sex_ratio_of_the_total_population_females_per_1000_males * Women_whose_Body_Mass_Index_BMI_is_below_normal_BMI__185_kgm214_ * Men_whose_Body_Mass_Index_BMI_is_below_normal_BMI__185_kgm2_ * Women_who_are_overweight_or_obese_BMI__250_kgm214_ * Men_who_are_overweight_or_obese_BMI__250_kgm2_ * All_women_age_1549_years_who_are_anaemic_ * Men_age_1549_years_who_are_anaemic_130_gdl_ * Women_Blood_sugar_level__high_140_mgdl_ * Men_Blood_sugar_level__high_140_mgdl_ * Women_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_ * Men_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_ </code></pre> lasso-KA+TN-bulletin This table contains data used for analysis on the sub-cohort of Karnataka and Tamil Nadu. Data from Media Bulletin <pre><code>* District * Total_Positives * total_deaths * male_deaths * female_deaths * Male_cases_in_data * Female_cases_in_data </code></pre> Calculated Data <pre><code>* Estimated_Male_cases - Estimated male cases using total positives column and existing case data * Estimated_Female_Cases - Estimated female cases using total positives column and existing case data * Male_Mortality - Estimated Male Cases / male_deaths * Female_Mortality - Estimated Female Cases / female_deaths </code></pre> Columns taken from NFHS data <pre><code>* Sex_Ratio_females_every_1000_males * State Women_whose_Body_Mass_Index_BMI_is_below_normal_BMI__185_kgm214_ * Men_whose_Body_Mass_Index_BMI_is_below_normal_BMI__185_kgm2_ * Women_who_are_overweight_or_obese_BMI__250_kgm214_ * Men_who_are_overweight_or_obese_BMI__250_kgm2_ * All_women_age_1549_years_who_are_anaemic_ * Men_age_1549_years_who_are_anaemic_130_gdl_ * Women_Blood_sugar_level__high_140_mgdl_ * Men_Blood_sugar_level__high_140_mgdl_ * Women_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_ * Men_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_ </code></pre> Code The code is available at this Github Repository.
DOI: 10.5281/zenodo.7934409
title: Sex-disaggregated Analysis of Risk Factors of COVID-19 Mortality Rates in India

description: This Zenodo resource contains the data used to perform analysis in the article "Sex-disaggregated Analysis of Risk Factors of COVID-19 Mortality Rates in India". Data The data is organized in the form of tables. hypothesis-test-data This table contains data used to perform the two tailed hypothesis test on gender mortality in different regions. <pre><code>* Region * Male_Deaths - Number of male COVID-19 deaths in region. * Female_Deaths - Number of female COVID-19 deaths in region. * Male_cases - Number of male COVID-19 positive in region. * Female_cases - Number of female COVID-19 positive in region. </code></pre> lasso-covid19India This table contains data used for analysis on cases throughout India. Columns from COVID-19 India data <pre><code>* State_Code * State * District * Confirmed * Active * Recovered * Deceased </code></pre> Columns taken from NFHS data <pre><code>* Sex_ratio_of_the_total_population_females_per_1000_males * Women_whose_Body_Mass_Index_BMI_is_below_normal_BMI__185_kgm214_ * Men_whose_Body_Mass_Index_BMI_is_below_normal_BMI__185_kgm2_ * Women_who_are_overweight_or_obese_BMI__250_kgm214_ * Men_who_are_overweight_or_obese_BMI__250_kgm2_ * All_women_age_1549_years_who_are_anaemic_ * Men_age_1549_years_who_are_anaemic_130_gdl_ * Women_Blood_sugar_level__high_140_mgdl_ * Men_Blood_sugar_level__high_140_mgdl_ * Women_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_ * Men_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_ </code></pre> lasso-KA+TN-bulletin This table contains data used for analysis on the sub-cohort of Karnataka and Tamil Nadu. Data from Media Bulletin <pre><code>* District * Total_Positives * total_deaths * male_deaths * female_deaths * Male_cases_in_data * Female_cases_in_data </code></pre> Calculated Data <pre><code>* Estimated_Male_cases - Estimated male cases using total positives column and existing case data * Estimated_Female_Cases - Estimated female cases using total positives column and existing case data * Male_Mortality - Estimated Male Cases / male_deaths * Female_Mortality - Estimated Female Cases / female_deaths </code></pre> Columns taken from NFHS data <pre><code>* Sex_Ratio_females_every_1000_males * State Women_whose_Body_Mass_Index_BMI_is_below_normal_BMI__185_kgm214_ * Men_whose_Body_Mass_Index_BMI_is_below_normal_BMI__185_kgm2_ * Women_who_are_overweight_or_obese_BMI__250_kgm214_ * Men_who_are_overweight_or_obese_BMI__250_kgm2_ * All_women_age_1549_years_who_are_anaemic_ * Men_age_1549_years_who_are_anaemic_130_gdl_ * Women_Blood_sugar_level__high_140_mgdl_ * Men_Blood_sugar_level__high_140_mgdl_ * Women_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_ * Men_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_ </code></pre> Code The code is available at this Github Repository.
DOI: 10.6084/m9.figshare.21294963
title: data

description: data
DOI: 10.6084/m9.figshare.21294963.v5
title: data

description: data
DOI: 10.6084/m9.figshare.22817321
title: Table 1 samples from Bey More 19E0405 (including processing and contextual information)

description: CSV file containing information about the samples taken during excavation at Bey More, Co. Meath. Processing and contextual information included.
DOI: 10.34740/kaggle/dsv/5682359
title: Egypt Gold Prices | Daily Updated
DOI: 10.6084/m9.figshare.22817336
title: Table 2 charred plant remains from Medieval Phase 1 deposits excavated at Bey More, Co. Meath 19E0405

description: CSV file containing details of identified charred plant remains from Bey More, Co. Meath, 19E0405
DOI: 10.6084/m9.figshare.22817342
title: Table 3 identified charred plant remains from Medieval Phase 2 deposits at Bey More, Co. Meath, 19E0405

description: CSV file of identified charred plant remains from Medieval Phase 2 deposits in Bey More, Co. Meath, 19E0405
DOI: 10.6084/m9.figshare.22817345
title: 不确定环境下再制造可重入柔性车间调度优化模型及求解算法研究

description: 这是 题为“不确定环境下再制造可重入柔性车间调度优化模型及求解算法研究”手稿的实验数据
DOI: 10.6084/m9.figshare.22817345.v1
title: 不确定环境下再制造可重入柔性车间调度优化模型及求解算法研究

description: 这是 题为“不确定环境下再制造可重入柔性车间调度优化模型及求解算法研究”手稿的实验数据
DOI: 10.6084/m9.figshare.22817348.v1
title: Table 4 identified waterlogged plant remains from moat samples excavated at Bey More, Co. Meath, 19E0405

description: CSV file containing identified waterlogged plant remains from moat deposits excavated at Bey More, Co. Meath, 19E0405
DOI: 10.6084/m9.figshare.22817348
title: Table 4 identified waterlogged plant remains from moat samples excavated at Bey More, Co. Meath, 19E0405

description: CSV file containing identified waterlogged plant remains from moat deposits excavated at Bey More, Co. Meath, 19E0405
DOI: 10.48324/dandi.000544/0.230514.1148
title: Test Dataset

description: Test dataset for DataJoint uploads to DANDI
DOI: 10.6084/m9.figshare.22817351
title: Table 5 identified waterlogged plant remains from latrine deposits excavated at Bey More, Co. Meath, 19E0405

description: CSV file detailing identified waterlogged plant remains from latrine deposits excavated at Bey More, Co. Meath, 19E0405
DOI: 10.6084/m9.figshare.22817351.v1
title: Table 5 identified waterlogged plant remains from latrine deposits excavated at Bey More, Co. Meath, 19E0405

description: CSV file detailing identified waterlogged plant remains from latrine deposits excavated at Bey More, Co. Meath, 19E0405
DOI: 10.6084/m9.figshare.22817354
title: group_size.csv

description: The sheet consists of chital group size observations near an Indian highway. Column no.s refer to S.no., Observation no., Date, Month, Year, Site (road type), Site2 (control or roadside), No. of chital individuals, distance to road (in m), and distance category.
DOI: 10.6084/m9.figshare.22817354.v1
title: group_size.csv

description: The sheet consists of chital group size observations near an Indian highway. Column no.s refer to S.no., Observation no., Date, Month, Year, Site (road type), Site2 (control or roadside), No. of chital individuals, distance to road (in m), and distance category.
DOI: 10.21227/fabw-q842
title: Thyroid Cancer

description: The medical community strives continually to improve the quality of care patients receive.Predictions of prognosis are essential for doctors and patients to choose a course of treatment. Recent yearshave witnessed the development of numerous new cancer survival prediction models. Most attempts topredict the prognosis of people with malignant development rely on classification techniques. We couldexperiment with significantly different results using only a subset of SEER (Surveillance, Epidemiology,and End Results) data. These models were created using machine learning techniques by selecting univariatefeatures and calculating correlations. We illustrated the variation in results and discrepancy of impuritythat can result from varying data quantities and critical factors. Seventeen crucial factors were identifiedto evaluate the effectiveness of an estimation technique. The most effective machine learning algorithms areLogistic Regression, Gradient Boosting Classifier, Random Forest, Extra Trees, Light Gradient Boost, AdaBoost Classifier, and Hist Gradient Boosting
DOI: 10.6084/m9.figshare.22817360
title: behaviour_metrics.csv

description: The datasheet contains behavioural observation on chital (Axis axis) under different classes, and four behavioural metrics near an Indian highway.
DOI: 10.6084/m9.figshare.22817360.v1
title: behaviour_metrics.csv

description: The datasheet contains behavioural observation on chital (Axis axis) under different classes, and four behavioural metrics near an Indian highway.
DOI: 10.6084/m9.figshare.19358819
title: Results from An Experimental Study about Gender-Stereotyped Gamification in Logic Tutoring Systems

description: Dataset with the responses gathered in an experimental study to assess the effect of gender-stereotyped gamification. These data include the following participants' information: disposition to negative thinking (pre.neg.thoughts), negative thinking (neg.thoughts), flow state (fss), disposition to flow state (dfs), learning performance (activityPoints). It also include the environment (testType) in which the students participated, their gender, and results from a socio-demographic questionnaire.<br>
DOI: 10.34740/kaggle/dsv/5682603
title: 2021 World Population (updated daily)
DOI: 10.6084/m9.figshare.22817357
title: Supporting data: Spotty strategy or smart survival? Vigilance behaviour of spotted deer <em>Axis axis</em> along a major highway in central India.

description: The dataset includes: 1. Mean time spent by chital on different behaviour types under 3 major behaviour classes near an Indian highway and in control habitat (behaviour_classes.csv), 2. Chital group sizes near highway and in control (group_size.csv), 3. Behaviour metrics calculated from time spent on different behaviours by chital (behaviour_metrics.csv).
DOI: 10.6084/m9.figshare.22817357.v1
title: behaviour_classes.csv

description: The excel sheet contains summarised mean time (in sec +- UCI, LCI) spent by chital on behaviour types (col. 3), under 3 categories of behaviour (col. 4), near roadside and control site (col.2).
DOI: 10.6084/m9.figshare.22817357.v2
title: Supporting data: Spotty strategy or smart survival? Vigilance behaviour of spotted deer <em>Axis axis</em> along a major highway in central India.

description: The dataset includes: 1. Mean time spent by chital on different behaviour types under 3 major behaviour classes near an Indian highway and in control habitat (behaviour_classes.csv), 2. Chital group sizes near highway and in control (group_size.csv), 3. Behaviour metrics calculated from time spent on different behaviours by chital (behaviour_metrics.csv).
DOI: 10.5281/zenodo.7933984
title: Evaluation of a simple score-based Natural Language Processing (NLP) algorithm: Result

description: The result for the experiment "Evaluation of a simple score-based Natural Language Processing (NLP) algorithm".
DOI: 10.5281/zenodo.7933974
title: Evaluation of a simple score-based Natural Language Processing (NLP) algorithm: Intermediary Result

description: The intermediary result of the experiment "Evaluation of a simple score-based Natural Language Processing (NLP) algorithm".
DOI: 10.34740/kaggle/dsv/5682688
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.5281/zenodo.7934375
title: Decomposition and nutrient dynamics of stumps and coarse roots of Eucalyptus plantations in southern China

description: <strong><em>Key message</em></strong>: <strong>Primary</strong><strong> remains of </strong><strong><em>Eucalyptus </em></strong><strong>(</strong><strong><em>E. urophylla × E. grandis</em></strong><strong>) </strong><strong>plantations</strong><strong> following clear-cutting are the stumps and coarse roots. </strong><strong>A chrono-sequence approach revealed that </strong><strong>the biomass of coarse roots is higher than that of stumps</strong><strong>,</strong><strong> while </strong><strong>they </strong><strong>ha</strong><strong>ve</strong><strong> a smaller rate of biomass loss than stumps. T</strong><strong>his</strong><strong> implies that coarse roots serve as long-term </strong><strong>stock</strong><strong> for</strong> <strong>nutrient</strong><strong>s</strong><strong> (</strong><strong>carbon, nitrogen</strong><strong>, and </strong><strong>p</strong><strong>hosphorus).</strong> <strong><em>C</em></strong><strong><em>ontext:</em></strong> A significant quantity of stumps and coarse roots persist in the forest floor following the clear-cutting of <em>Eucalyptus</em> plantations in China. The decomposition of these stumps and coarse roots is a crucial aspect of the nutrient stocks in plantations ecosystems. <strong><em>A</em></strong><strong><em>ims:</em></strong> We described the stock and decomposition dynamics of stumps and coarse roots on <em>Eucalyptus</em> plantations, as well as the nutrient loss rate associated with them. <strong><em>Methods:</em></strong> We used a chrono-sequence of 0–6-year-old clear-cutting stands of <em>Eucalyptus</em> plantations in southern China. The biomass and nutrient stocks of these samples were assessed at different times of decomposition. <strong><em>Results:</em></strong> The highest biomass of stump-root system was 33.8 Mg·ha<sup>-1</sup>, with nutrient stocks were 16.3 Mg·ha<sup>-1</sup>, and 63.0 kg·ha<sup>-1</sup>, and 6710.9 g·ha<sup>-1</sup>, for C, N, and P. The coarse roots accounted for 81–96% of the biomass of the stump-root system. The loss rate of biomass and nutrients from stumps was higher than that of coarse roots. This suggests that the decomposition of coarse roots could serve as a long-term source of nutrients, thereby improving the nutrient status of the plantations. <strong><em>Conclusion:</em></strong> Stumps and coarse roots serve as significant nutrients stocks that decay at varying rates. The loss of nutrients must be considered while analyzing the decomposition dynamics following clear-cutting in <em>Eucalyptus</em> plantations. This dataset is related to a research paper submitted to Annals of Forest Science.
DOI: 10.5281/zenodo.7934374
title: Decomposition and nutrient dynamics of stumps and coarse roots of Eucalyptus plantations in southern China

description: This study aimed to (1) quantify the stocks of biomass and nutrients from <em>Eucalyptus</em> stumps and coarse roots along a chrono-sequence of 0–6-year-old clear-cutting stands and (2) estimate the loss rate of biomass and nutrients, as well as the nutrient dynamics of stumps and coarse roots, to elucidate (1) if the biomass of coarse roots is higher than that of stumps and (2) if there is faster decomposition and nutrient loss rate from stumps from than coarse roots. <strong><em>Conclusion:</em></strong> Stumps and coarse roots serve as significant nutrients stocks that decay at varying rates. The loss of nutrients must be considered while analyzing the decomposition dynamics following clear-cutting in <em>Eucalyptus</em> plantations. This dataset is related to a research paper submitted to Annals of Forest Science.
DOI: 10.5281/zenodo.7934507
title: Decomposition and nutrient dynamics of stumps and coarse roots of Eucalyptus plantations in southern China

description: This study aimed to (1) quantify the stocks of biomass and nutrients from <em>Eucalyptus</em> stumps and coarse roots along a chrono-sequence of 0–6-year-old clear-cutting stands and (2) estimate the loss rate of biomass and nutrients, as well as the nutrient dynamics of stumps and coarse roots, to elucidate (1) if the biomass of coarse roots is higher than that of stumps and (2) if there is faster decomposition and nutrient loss rate from stumps from than coarse roots. <strong><em>Conclusion:</em></strong> Stumps and coarse roots serve as significant nutrients stocks that decay at varying rates. The loss of nutrients must be considered while analyzing the decomposition dynamics following clear-cutting in <em>Eucalyptus</em> plantations. This dataset is related to a research paper submitted to Annals of Forest Science.
DOI: 10.5517/ccdc.csd.cc2b8jw1
title: CCDC 2153163: Experimental Crystal Structure Determination

description: Related Article: Fei Gou, Di Shi, Bohan Kou, Zhao Li, Xiaosheng Yan, Xin Wu, Yun-Bao Jiang|2023|J.Am.Chem.Soc.|145|9530|doi:10.1021/jacs.2c11684
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.