cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.29062814.v1
title: tropical forest soil microbial community in China

description: Between 30% and 50% of the carbon (C) respired from tropical forests originates from soil, most of which is derived from the decomposition of organic matter. Subsoils contain more than half of the global soil organic C stocks, which have much slower turnover compared to the soil organic C in topsoil. Because of very slow process rates, the temperature sensitivity (Q<sub>10</sub>) of subsoil organic C decomposition to rainfall changes is nearly unknown. Therefore, we investigated the responses of Q<sub>10</sub>, soil organic acids, microbial communities and necromass of tropical forest soil to a delayed and wetter rain season in a 10-year manipulation experiment. Microbial respiration absence of differences in response to shifts in rainfall patterns. The delayed rain season did not affect Q<sub>10</sub>, soil organic acids, bacterial and fungal communities and microbial necromass in 0–70 cm soil layers. Wetter rain season significantly increased the Q<sub>10</sub>, which mainly due to wetter rain season increased C inputs and resulted in nitrogen limitation, and subsequently contribute to the decomposition of subsoil organic C. The concentrations of acetic acid and the relative abundance of Glomeromycota in 30-50 and 50–70 cm soil layers. Q<sub>10</sub> was positively associated with acetic acid and the relative abundance of Glomeromycota, and was negatively associated with bacterial necromass/soil organic C. Rare Glomeromycota taxa was the best microbial predictor in predicting the response of Q<sub>10</sub> to rainfall changes in tropical forests. Thus, our study suggested that rising temperature may trigger deep soil C loss from tropical forests under rainfall changes.
DOI: 10.17632/358bfdjvwg
title: Atomic Mass – Energy-Primary Colour

description: Lot of out of text question arise in childhood mind during study in between different problems. Like – E=mc2 ; then ,what is the relation of E (energy) with “c2”? Different food item, Bettery;etc. have energy; in which “c2 or,the reation of the speed of ligft with it , or,why the orbit of electron of an Atom is like this etc. 

The calculations or, formulae which have shown in this is based on Every element are created from the collaboration of different energy. This energy known as heat energy, magnetic energy , light energy etc.

            Since then, many more articles have been published in data format in support of this article and in a (strongly) scientifically sound manner, arguing that the quantum numbers used in this article and all the other mathematical calculations used in this article are not unreasonable or unrealistic. Where we can find out many unknown facts of science by placing the calculations obtained in my article in place of different numbers or units used in modern science. 
E.g, As in all these articles, the source of Pi has been obtained in different ways or the sources of numbers like 22, 7, 9, etc., so if we put the calculation of 22 and  7 sources in place of Pi, we will understand why Pi has been used as mentioned. Or what is the secret of Pi.  So I request all the readers to read every article written by me. 
All of these articles can be found by searching Google or Mendeley  Data. 
There are also many well-known organizations and social media (like Google - Blog, Twitter, Facebook, etc)   around the world who are relentlessly striving to advance science, exploring every article I write.
 Explore at ‘narcis.nl, easy.dans.knaw.nl,  mendeley.figshare.com,  datacite.com,  commons datacite.com, datasetsearch.research.google.com, semanticscholar.org, b2find.eudat.eu, etc.
DOI: 10.17632/jv36jsfd8v
title: Panel survey data on the investment and risk behavior of Neobroker users in Germany

description: Description:
This dataset comprises comprehensive data collected from a longitudinal study involving three waves of surveys conducted among Neobroker users in Germany. The surveys were administered over 14 months, specifically in December 2022, August 2023, and February 2024. Each survey wave utilized specific criteria to engage targeted groups such as current and former Neobroker users, general online investors, and those intending to use Neobrokers in the future. The data offers insightful perspectives into investment and risk behaviors, notably focusing on how individuals interact with trading apps.

Research Hypothesis:
The core research hypothesis revolves around understanding the influence of trading apps on investment decisions and risk-taking behaviors over time. The study aims to determine how these applications affect user behavior across different demographic segments and compare characteristics between Neobroker users and general online investors.

Data Collection and Methodology:
Data gathering was executed by a survey company that specializes in representative online surveys within Germany, in collaboration with academic researchers who developed the questionnaires. The survey design incorporated randomization and control questions to maintain response integrity and minimize biases. The sampling methodology was stratified to enhance representativeness across the respective target groups (e.g. Neobroker users, general investors, Ex-Neobroker users). The process ensured comprehensive coverage of participant profiles within each target group.

Interpreting the Data:
The dataset is structured to facilitate analysis across various user engagement and investment behavior metrics. The datasets from each wave vary slightly due to the unique composition of participant groups: Neobroker users, prospective users, former users, and general investors. Participants from previous waves are consistently tracked to enable robust longitudinal analysis. Dataset 2 and 3 therefore contain repeatedly surveyed Neobroker users.
DOI: 10.6084/m9.figshare.28774625.v1
title: FastAAI Codebase

description: FastAAI is an ultrafast estimator of whole-genome aggregate amino acid identity (AAI). FastAAI utilizes tetramer similarities across matched pairs of marker genes to estimate AAI in a computationally simple manner, achieving orders of magnitude speedup over alignment and phylogenetic approaches with a minimal lack of accuracy.
DOI: 10.6084/m9.figshare.29126426.v1
title: Tissue specific genes for each location of muscle tissue

description: Specific expression genes of muscles in different sampling locations of sepsis model pigs.
DOI: 10.6084/m9.figshare.6621110.v1
title: Supporting data from Frequency dispersion reveals chromophore diversity and colour-tuning mechanism in parrot feathers

description: Raman spectra and additional information
DOI: 10.6084/m9.figshare.12494153.v1
title: Data from: Evaluation of camera placement for detection of free-ranging carnivores; implications for assessing population changes

description: Contains all data necessary to duplicate the analyses described by Geyle et al., 2020 including the date and time of all detections of cats and foxes at both camera locations (on-road and off-road) within the Grampians National Park (Vic, Australia) over five seasons of sampling (winter 2016, autumn 2017, spring 2017, autumn 2018 and autumn 2019) ("all data" tab). We also provide information on missing data across seasons ("site information by season" tab) and provide a key to column headers ("description" tab). <br>
DOI: 10.6084/m9.figshare.12824165.v1
title: Data from Photosynthetic heat tolerances and extreme leaf temperatures

description: Observed leaf temperature, environmental data, leaf thermoregulatory, and heat tolerance data from Perez, T. M., & Feeley, K. J. (2020). Photosynthetic heat tolerances and extreme leaf temperatures. <i>Functional Ecology</i>. https://doi.org/10.1111/1365-2435.13658 <br>
DOI: 10.6084/m9.figshare.15041461.v2
title: All of the supplementary tables from Forensic characteristics and population construction of two major minorities from southwest China revealed by a novel 37 Y-STR loci system

description: Table S1 Allele frequencies and gene diversity of Miao population; Table S2 Allele frequencies and gene diversity of Tujia population; Table S3 Haplotypes of Miao population; Table S4 Haplotypes of Tujia population; Table S5 Pairwise Rst
DOI: 10.6084/m9.figshare.23959320.v3
title: data for "meta-analysis highlights globally widespread potassium limitation in terrestrial ecosystems"

description: A meta-analysis of 95 published research articles and compiled a database comprised of 144 K fertilization experiments.
DOI: 10.6084/m9.figshare.27049285.v1
title: Hourly carbon emission factors of 31 provinces and cities in China from 2025 to 2060

description: Hourly carbon emission factors of 31 provinces and cities in China from 2025 to 2060
DOI: 10.6084/m9.figshare.27092008.v1
title: A sentence-level sentiment analysis of some literary texts inEnglish language

description: We perform a sentence-level sentiment analysis study of different literary texts in English language. Each text is converted into a series in which the data points are the sentiment value of each sentence. By applying the Detrended Fluctuation Analysis (DFA) and the Higuchi Fractal Dimension (HFD) methods to these sentiment series, we find that they are monofractal with long-term correlations, which can be explained by the fact that the writing process has memory by construction, with a sentiment evolution that is self-similar. Furthermore, we discretize these series by applying a classification approach which transforms the series into a one on which each data point has only three possible values, corresponding to positive, neutral or negative sentiments. We map these three-states series to a Markov chain and investigate the transitions of sentiment from one sentence to the next, obtaining a state transition matrix for each book that provides information on the probability of transitioning between sentiments from one sentence to the next. This approach shows that there are biases towards increasing the probability of switching to neutral or positive sentences. The two approaches supplement each other, since the long-term correlation approach allows a global assessment of the sentiment of the book, while the state transition matrix approach provides local information about the sentiment evolution along the text.
DOI: 10.6084/m9.figshare.8796944.v3
title: FosSahul 2.0 database and R code

description: <i>FosSahul</i> 2.0 database and R code accompanying manuscript "<i>FosSahul </i>2.0, an updated database for the Late Quaternary fossil records of Sahul" submitted to <i>Scientific Data</i>. Excel files: FosSahul2.0.csv: <i>FosSahul</i> database collating non-human vertebrate megafauna fossil records for the Sahul region. Note that location data have been rounded to one degree decimal and might not reflect the exact location of the fossil record. For more information on precise locations, contact the authors.FosSahul2.0_metadata.xlsx: Column description and further detail on the <i>FosSahul</i> 2.0 database.CalibratedC14Dates_FosSahul.csv: Calibrated radiocarbon dates for <i>FosSahul</i> 2.0. Needed for the calculation of the biodiversity index.TimeBins.csv: Time bins needed for the calculation of the biodiversity index. R-scripts: FosSahul_Rating.R: Quality-rating algorithm for the <i>FosSahul</i> database.FosSahul_Data import.R: Data import script necessary for the calculation of the biodiversity index.FosSahul_Biodiversity_index_calculation.R: Code for the calculation of the biodiversity index.
DOI: 10.25442/hku.23989617.v1
title: Global 1028 cities boundaries and names

description: 1. Global_1028_cities_urban_boundary.shpThis data represents the continuous built-up areas for the 1028 cities selected in this study.2. Global_1028_cities_names.xlsxThis data represents the names for global 1028 cities <br>
DOI: 10.6084/m9.figshare.11409447.v1
title: Dissociating representations of time and number in reinforcement rate learning by GluA1 AMPAR subunit deletion in mice

description: Data for Experiments 1-8 in "Dissociating representations of time and number in reinforcement rate learning by GluA1 AMPAR subunit deletion in mice". · The data for each experiment is provided in separate tabs. · The first three columns of each tab list the ID, sex and genotype of the subjects.· For Experiments 1-6 the fourth column of each tab lists the modality (auditory/visual) of one of the cue conditions in the experiment. This factor was included in the analyses in order to control for variance caused by the counterbalancing of modality across conditions.· For Experiments 1-7 the difference score for each condition per block is listed as rate per minute. The column title indicates the cue condition and block.· For Experiment 8, columns D-AG list the mean rate of responding for each second of the nonreinforced probe trials. Response rates are per second. Column AH lists the slopes for normalized response rates for the first 10 s all trials. Column AI lists the measure of spread taken for Gaussian curve fitting analysis across response rates for the probe trials. · The data for each experiment is provided in separate tabs. · The first three columns of each tab list the ID, sex and genotype of the subjects.· For Experiments 1-6 the fourth column of each tab lists the modality (auditory/visual) of one of the cue conditions in the experiment. This factor was included in the analyses in order to control for variance caused by the counterbalancing of modality across conditions.· For Experiments 1-7 the difference score for each condition per block is listed as rate per minute. The column title indicates the cue condition and block.· For Experiment 8, columns D-AG list the mean rate of responding for each second of the nonreinforced probe trials. Response rates are per second. Column AH lists the slopes for normalized response rates for the first 10 s all trials. Column AI lists the measure of spread taken for Gaussian curve fitting analysis across response rates for the probe trials.
DOI: 10.6084/m9.figshare.22344706.v3
title: Do Automatic Test Generation Tools Generate Flaky Tests?

description: Published at the 46th International Conference on Software Engineering (ICSE 2024). Here you can find a preprint.About the artifacts<b>dataset.csv.gz</b>each row represents one test casecolumn "test_type": was the generated or developer-writtencolumn "flaky": has the test shown flaky behavior, and what kind? (NOD = non-order-dependent, OD = order-dependent)used to answer RQ1 (Prevalence) and RQ2 (Flakiness Suppression).<b>LoC.zip</b>contains lines-of-code data for the Java and Python projects<b>flaky_java_projects.zip</b> and <b>flaky_python_projects.zip</b>archives containing the 418 Java and 531 Python projects that contained at least one flaky testeach project contains the developer written and generated test suites<b>manual_rootCausing.zip</b>results of the manual root cause classificationfull_sample.csvcolumn "rater": which of the four researchers conducting the classification rated this test (alignment = all four)used to answer RQ3 (Root Causes)Running the jupyter notebookDownload all artifactsCreate and activate virtual environment<br><code>virtualenv -p venv</code><br><code>source venv/bin/activate</code>Install dependencies<br><code>pip install -r requirements.txt</code>Start jupyter lab<br><code>python -m jupyter lab</code>Scripts used for test generation and execution<br>Java (EvoSuite)Python (Pynguin)
DOI: 10.6084/m9.figshare.26063605.v1
title: Raw scale values (.csv) used to run our pipeline

description: This is the dataset we used as the input for our analysis pipeline in this manuscript. It can be used to run the pipeline.
DOI: 10.6084/m9.figshare.26490769.v1
title: labeled_Data

description: labeled_Data
DOI: 10.17632/9hwmv8t36b
title: Spillover Effects of Green Human Resource Management on Employee Health from the Perspective of Organizational Change

description: The data collection period was from August to November 2020. A cross-sectional survey was employed and an online questionnaire was distributed to employees in China. The data were analyzed by structural equation model (SEM) and fuzzy-set qualitative comparative analysis (fsQCA).
DOI: 10.17632/tp95cdvgm8
title: An Annotated Water-Filled, and Dry Potholes Dataset for Deep Learning Applications

description: Dataset Content:
- IMG -> Dataset Images.
- XML -> Pascal VOC XML Format Annotations.
- TXT -> YOLO TXT Format Annotations.
- Potholes.zip -> Dataset images and annotation files in compressed format.

Video:
- Dashcam-Front.mp4 ->  A short outdoor front car dashcam video showing the coordinates and car speed to assess the frame rate and accuracy of the detection (faces and plate numbers blurred for privacy).
- Dashcam-Rear.mp4 ->  A short outdoor rear car dashcam video showing the coordinates and car speed to assess the frame rate and accuracy of the detection (faces and plate numbers blurred for privacy).


Note: train.txt and valid.txt files are to be generated based on the location of the dataset files within the training environment.
DOI: 10.17632/yzjm44f6vt
title: Lonely Human and Dominant Robot

description: While social robots offer convenient opportunities for interaction, people may not readily embrace artificial companionship as a substitute for human companionship. This research examines an overlooked question: How can we increase the likelihood of potential users, particularly those experiencing loneliness, developing meaningful connections with robots to satisfy their social needs? Through six studies, we found that individuals who are experiencing greater loneliness tend to hold less favorable attitudes toward companion robots but are more likely to bond with submissive-acting robots over dominant-acting robots.
DOI: 10.6084/m9.figshare.17029565.v1
title: LogFile_Events.xlsx

description: Log File of events generated by an emulator that mimics the behavior of an outpatient clinic in a hospital.
DOI: 10.6084/m9.figshare.18972047.v2
title: Wind and runoff/sediment discharge of the Yellow River.xlsx

description: Sheet ”runoff of the Yellow River” contains the monthly runoff of the Yellow River data (unit: × 10<sup>8</sup> m<sup>3</sup>) from January 2003 to December 2016 (Lijin Station). Sheet “sediment discharge” contains the monthly sediment discharge of the Yellow River data (unit: × 10<sup>6</sup> t) from January 2003 to December 2016 (Lijin Station). Sheet “wind speed” contains the monthly wind speed data (unit: m/s) measured at 14 meteorological stations (Changdao, Dawa, Dongying, Fushan, Huanghua, Kenli, Laoting, Longkou, Suizhong, Tanggu, Weifang, Xingcheng, Xiongyue, and Yingkou station) around the Bohai Sea from January 2003 to December 2016. Sheet ”wind direction” contains the monthly wind direction data (unit: °) measured at 13 meteorological stations (Changdao, Dawa, Fushan, Huanghua, Kenli, Laoting, Longkou, Suizhong, Tanggu, Weifang, Xingcheng, Xiongyue, and Yingkou station) around the Bohai Sea from January 2003 to December 2016.
DOI: 10.6084/m9.figshare.19643070.v1
title: Appendix - logistic regression analysis basic and full model for FRAIL and FI BP groups.docx

description: This is the extended data containing the appendix for the paper ''Older adults identified as frail by Frailty Index and FRAIL scale who were intensively treated for hypertension were at increased risk of 2-year adverse health outcomes in The Irish Longitudinal Study on Ageing (TILDA)'' that is in the process of being submitted to HRB Open journal. <br>
DOI: 10.6084/m9.figshare.5121709.v1
title: Supplementary Material for: Überprüfung des aktuellen Stressbewältigungsverhaltens bulimischer Frauen in einer sozialen Interaktion

description: <i>Hintergrund:</i> Frühere Studien zeigten ein dysfunktionales Stressbewältigungsverhalten für Frauen mit Essstörungen. Insbesondere ein geringeres Maß an problem- und ein erhöhtes Maß an emotions- und vermeidungsorientiertem Stressbewältigungsverhalten im Vergleich zu Gesunden wurde beschrieben. Die meisten Untersuchungen fokussierten dabei auf habituelles Coping, während die Untersuchung von aktuellem, situationsbezogenem Coping bisweilen aussteht. Unklar ist zudem, inwieweit aktuelles, situationsbezogenes Coping durch das Selbstwertgefühl, dysfunktionale Einstellungen und Alexithymie beeinflusst wird. <i>Methode:</i> Mittels des Stress-Verarbeitungs-Fragebogens 42-aktuell (SVF 42-ak) wurde das aktuelle Stressbewältigungsverhalten bei 26 Frauen mit Bulimia nervosa (BN) und 27 weiblichen Personen einer Kontrollgruppe (KG) in einem sozial ambivalenten Rollenspiel untersucht. Der Einfluss des Selbstwerts (Rosenberg Selfesteem Scale, RSS), dysfunktionaler Einstellungen (Dysfunctional Attitude Scale, DAS) und von Schwierigkeiten der Emotionswahrnehmung (Toronto Alexithymia Scale, TAS) auf das aktuelle Coping wurde überprüft. <i>Ergebnisse:</i> Die BN-Gruppe zeigte im Vergleich zur KG-Gruppe ein erhöhtes Maß an aktuellem, emotionalen Coping (d = 1,96). Im problem- (d = 0,31) und vermeidungsorientierten Coping (d = 0,27) ergaben sich keine Unterschiede. Der Selbstwert als Hauptprädiktor klärte in der Regressionsanalyse nahezu 70% (R2 = 0,676) der Varianz des emotionalen Copings auf und korrelierte signifikant mit dem Grad dysfunktionaler Einstellungen (r = –0,791) und Alexithymie (r = –0,782). <i>Schlussfolgerung:</i> Die Ergebnisse sprechen für einen übergeordneten Einfluss des Selbstwerts auf das aktuelle Coping.
DOI: 10.6084/m9.figshare.7603442.v1
title: L1 muts in MG.xlsx

description: minigenome assay to test the function of L1 mutants in viral RNA synthesis
DOI: 10.17632/njz7cffzw2
title: Misery index and outbound tourists: Data for Mexico 

description: This document contains data to study the relationship between outbound tourists and the compensated misery index (CMI) proposed by Hortalà and Rey (2011). 

I have computed an Unrestricted VAR model by using the CMI that I originally calculated to study the relationship between the number of international visitors in Mexico and the CMI, which was first shared in Sánchez (2021) (DOI: 10.17632/hzgysvg55k.1), and that I share in this document once again to have all the model variables in a single document. It is important to mention that no change has been done to the file containing the CMI. 

To compute the model, I have also obtained the multilateral real exchange rate index with respect to 111 eleven countries and the number of outbound tourists. I used two exogenous variables to calculate the VAR. These variables are named Dummy 1 and Dummy 2. 

Finally, I also share the Eviews file where I have computed the model.