cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5286/isis.e.rb2590201
title: Kink in the electronic structure of MnSb2Te4 : Band feature or electron-magnon coupling?

description: MnSb2Te4 is a promising magnetic topological insulator candidate, composed of septuple layers stacked along the c-axis with van der Waals gaps in between. The Mn2+ ions order ferrimagnetically at 32 K. Currently, there is only one published INS study from Oak Ridge that measured low-energy magnons (0-20 meV) in a powder sample.

Our group has conducted several experimental studies using angle-resolved photoemission spectroscopy (ARPES) on MnSb2Te4 single crystals with TC = 32 K. Our data not only supports the existence of nontrivial topology but also reveals for the first time a strong coupling between the magnetic ground state and the topological surface states. Such strong coupling is unprecedented in the literature on magnetic topological materials and could be explained by considering the role of magnon-mediated interactions in the electronic structure. However, this coupling is observed at a higher energy scale (~150 meV), and the lack of INS data in this range prevents us from unambiguously linking the observed features to electron-magnon coupling.

We would like to perform a quick screening measurement on a ~6 g powder sample of MnSb2Te4 using INS, ideally covering the 0 200 meV energy range at one temperature T &lt; 32 K. Our main goal is to look for any signal of magnons in the ~150 meV region that could support the ARPES observations and help distinguish whether the kink we observe is a signature of magnon-mediated interactions.
DOI: 10.34740/kaggle/dsv/13056835
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.6069/xezp-6j22
title: Quantitative protein analysis and glycan profiling of umbilical cord plasma and corresponding maternal plasma samples

description: Protein glycosylation is an essential post-translational modification boosting proteomic diversity. The major groups of human plasma glycoproteins are immunoglobulins (Igs) and acute phase proteins (APPs). Changes in plasma Igs and APPs levels and their glycosylation have been previously associated with inflammatory diseases, several types of cancer, pregnancy, or aging. In this study, we present a novel approach to investigate the neonatal and maternal plasma at the end of pregnancy using a combination of targeted quantitative proteomics and total plasma N-glycome profiling. We present a new SRM-based multiplex assay to quantify 19 APPs and 7 Igs. We applied this assay to 98 samples of umbilical cord plasma and corresponding maternal plasma samples and observed significantly lower levels of all analyzed proteins in neonates, apart from the glycoprotein alpha-2-macroglobulin. Unlike previous methods, our SRM-MS assay allows us to quantify dozens of proteins simultaneously and to distinguish protein variants. We compared maternal and fetal total plasma-released N-glycomes in the same set of samples. We quantified 66 individual glycans, which were used to calculate 73 glycosylation traits. In neonates, the major N-glycans were di-antennary complex-type with core fucosylation, and the production of higher, branched, sialylated N-glycans was limited.
DOI: 10.57760/sciencedb.26923
title: Main vegetation types and species composition of Daluo Mountain, Wenzhou, Zhejiang, China
title: 温州大罗山主要植被类型及物种组成特征

description: Daluo Mountain, located in the southeastern Wenzhou City, lies within the subtropical evergreen broadleaf forest zone. Despite its ecological significance, comprehensive vegetation data for this region remain limited. This study aims to investigate the main vegetation types on Daluo Mountain, focusing on species composition, community characteristics, and distribution patterns. This study employs standard sampling methods to investigate the vegetation. A total of 85 plots were established across Daluo Mountain. Data were collected on species composition, quantitative attributes, and habitat characteristics. Vegetation types were classified and named, resulting in the Daluo Mountain plant community dataset. The survey identified 244 plant species from 179 genera and 96 families. The most species-rich families were Poaceae (23 species), Rosaceae (13 species), Fabaceae (12 species), Asteraceae (7 species), and Cupressaceae (6 species). The most species-rich genera include Ilex (6 species), Symplocos (5 species), Rubus (5 species), Smilax (4 species), and Persicaria (4 species). The vegetation was categorized into 6 Vegetation Formation Groups, 16 Vegetation Formations, and 56 Alliances, providing fundamental data for studying the vegetation characteristics of Daluo Mountain.
description: Daluo Mountain, located in the southeastern Wenzhou City, lies within the subtropical evergreen broadleaf forest zone. Despite its ecological significance, comprehensive vegetation data for this region remain limited. This study aims to investigate the main vegetation types on Daluo Mountain, focusing on species composition, community characteristics, and distribution patterns. This study employs standard sampling methods to investigate the vegetation. A total of 85 plots were established across Daluo Mountain. Data were collected on species composition, quantitative attributes, and habitat characteristics. Vegetation types were classified and named, resulting in the Daluo Mountain plant community dataset. The survey identified 244 plant species from 179 genera and 96 families. The most species-rich families were Poaceae (23 species), Rosaceae (13 species), Fabaceae (12 species), Asteraceae (7 species), and Cupressaceae (6 species). The most species-rich genera include Ilex (6 species), Symplocos (5 species), Rubus (5 species), Smilax (4 species), and Persicaria (4 species). The vegetation was categorized into 6 Vegetation Formation Groups, 16 Vegetation Formations, and 56 Alliances, providing fundamental data for studying the vegetation characteristics of Daluo Mountain.
DOI: 10.5281/zenodo.17116856
title: Individual Civilization Health Check v1.0 with Appendices   (扩展自文明九大公理第四公理:结构必然)

description: This dataset contains the "Individual Civilization Health Check Model v1.0" with appendices, 

expanding upon the Fourth Civilizational Axiom: "Structure is Inevitable." 

It provides a quantitative framework for evaluating individuals as functional or cancerous cells 

within civilization, using structural indicators derived from the Nine Civilizational Axioms.  

 

Contents include:  

• A detailed scoring system (FFI, CSI, CHS, EOI, RUI, HCCI).  

• Forty structural indicators for individual assessment.  

• Appendices illustrating case studies and civilization health checks.  

 

Author: Kong Weicheng (孔维程)  

License: CC0 1.0 Universal (Public Domain Dedication)

🌐 Permanent Mirrors · 永久镜像

 

GitHub Pages (Master Entry): https://wk-26.github.io/Civilization-Axioms-and-Immune-System-/

 

GitHub Repository: https://github.com/wk-26/Civilization-Axioms-and-Immune-System-

 

Internet Archive: https://archive.org/details/a-new-civilization-for-humanity-cc-0

 

Zenodo DOI (all versions): https://doi.org/10.5281/zenodo.16980277

 

🔗 Core TXT Links · 九大公理 TXT 直链  

 

- GitHub TXT: https://wk-26.github.io/Civilization-Axioms-and-Immune-System-/Nine%20Civilizational%20Axioms%20and%20the%20AI%20Immune%20Framework.txt  

 

- Archive TXT: https://archive.org/download/a-new-civilization-for-humanity-cc-0/Nine%20Civilizational%20Axioms%20and%20the%20AI%20Immune%20Framework.txt  

 

- Zenodo TXT: https://zenodo.org/record/16980277/files/Nine%20Civilizational%20Axioms%20and%20the%20AI%20Immune%20Framework.txt

 

📜 License Declaration · 授权声明:

 

As of August 29, 2025, to the extent permitted by law,

 

Wei Cheng Kong (WK, 孔维程) has permanently waived all copyright and related rights in all of his works.

 

The only valid license is CC0 1.0 Universal (Public Domain Dedication).
DOI: 10.5281/zenodo.14948869
title: In-situ continuous measurement of methane concentration and d13C along the peat profile and in the emitted CH4 at a boreal mire

description: This contains measurement data of dissolved methane concentration along peat profile,  d13C-CH4 along peat profile and in the emitted CH4, as well as leaf area index measured at Siikaneva fen (Southern Finland) during a full annual cycle (16 March 2022 and 15 March 2023).
DOI: 10.5281/zenodo.17116840
title: In-situ continuous measurement of methane concentration and d13C along the peat profile and in the emitted CH4 at a boreal mire

description: This contains measurement data of dissolved methane concentration along peat profile,  d13C-CH4 along peat profile and in the emitted CH4, as well as leaf area index measured at Siikaneva fen (Southern Finland) during a full annual cycle (16 March 2022 and 15 March 2023).
DOI: 10.6084/m9.figshare.30122176
title: Data from "Drivers of larval growth and settlement success: The role of environmental and early life history traits in the blenny <i>Ophioblennius trinitatis</i>"

description: This dataset contains monthly environmental and biological data collected from January 2011 to April 2016 in the Tamandaré Reef Complex, off northeastern Brazil in the Western Tropical Atlantic. The data were obtained to investigate the influence of environmental variability on the abundance of pre-settlement <i>Ophioblennius trinitatis</i> larvae captured with light traps.The dataset includes the following variables: <b>Year</b> – sampling year; <b>Month</b> – sampling month; <b>Season</b> – austral season of the sampling period (Summer, Fall, Winter, Spring); <b>Sea surface temperature (°C)</b> – average monthly sea surface temperature; <b>Sea surface salinity</b> – average monthly sea surface salinity; <b>Wind direction (incident angle)</b> – predominant wind direction expressed in degrees; <b>Wind speed (m s⁻¹)</b> – average monthly wind speed; <b>Rainfall (mm m⁻²)</b> – monthly accumulated precipitation; <b>Chlorophyll-a (mg m⁻³)</b> – monthly mean surface chlorophyll-a concentration; <b>Total abundance of pre-settlement larvae</b> – total number of reef fish pre-settlement larvae collected per month.
DOI: 10.6084/m9.figshare.30122176.v1
title: Data from "Drivers of larval growth and settlement success: The role of environmental and early life history traits in the blenny <i>Ophioblennius trinitatis</i>"

description: This dataset contains monthly environmental and biological data collected from January 2011 to April 2016 in the Tamandaré Reef Complex, off northeastern Brazil in the Western Tropical Atlantic. The data were obtained to investigate the influence of environmental variability on the abundance of pre-settlement <i>Ophioblennius trinitatis</i> larvae captured with light traps.The dataset includes the following variables: <b>Year</b> – sampling year; <b>Month</b> – sampling month; <b>Season</b> – austral season of the sampling period (Summer, Fall, Winter, Spring); <b>Sea surface temperature (°C)</b> – average monthly sea surface temperature; <b>Sea surface salinity</b> – average monthly sea surface salinity; <b>Wind direction (incident angle)</b> – predominant wind direction expressed in degrees; <b>Wind speed (m s⁻¹)</b> – average monthly wind speed; <b>Rainfall (mm m⁻²)</b> – monthly accumulated precipitation; <b>Chlorophyll-a (mg m⁻³)</b> – monthly mean surface chlorophyll-a concentration; <b>Total abundance of pre-settlement larvae</b> – total number of reef fish pre-settlement larvae collected per month.
DOI: 10.5281/zenodo.17116871
title: Santorini
DOI: 10.5281/zenodo.17116872
title: Santorini
DOI: 10.5281/zenodo.17116882
title: Seismic and simulation data for "Stress Interactions Among Strong-Weak Fault Patches  Govern Earthquake Cycle"

description: This dataset contains all the observations from Gofar transform fault in 2008 and simulation data generated in the study “Stress Interactions Among Strong-Weak Fault Patches  Govern Earthquake Cycle”. Included files: 1. Observation data; 2. Simulation data; 3. README.md.
DOI: 10.6084/m9.figshare.30068224.v2
title: <b>绘制温带天然草原的牧草营养质量图谱</b><b>interpretable machine learning</b>

description: 支持论文发现的其余数据和代码（<b>绘制温带天然草原的牧草营养质量）。 </b><b>可解释机器学习</b><b>).</b>
DOI: 10.6084/m9.figshare.30122197
title: Temperature Data

description: Spreadsheets with temperature data obtained from IR thermal imaging
DOI: 10.6084/m9.figshare.30122197.v1
title: Temperature Data

description: Spreadsheets with temperature data obtained from IR thermal imaging
DOI: 10.3886/e237921
title: Lonely Hearts & Artificial Minds: A Survey Study on Relationships with AI Companions and AI Assistants

description: This study compares the characteristics and motivations of users of AI companions with users of AI assistants. A cross-sectional survey examined demographic factors, psychological characteristics, and motivational patterns across the two user groups through a cross-sectional survey of 230 respondents. The findings reveal distinct patterns in usage, motivations, and user characteristics between the two types of AI chatbots. Age emerged as a significant predictor with contrasting effects: older individuals showed a higher frequency of AI companion usage and lower AI assistant usage. Users of AI companions demonstrated higher levels of loneliness and lower life satisfaction, while these traits were not significant for AI assistant users. The motivational analysis demonstrated significantly higher scores for all romantic motivations to use AI companions, with the most pronounced differences among emotional support, feeling loved, care for others, and sexual satisfaction, compared to AI assistants. Motivations for psychological growth and emotional support predicted the frequency of AI companion usage. There were no significant motivational predictors for the frequency of AI assistant usage. Mediation analyses revealed that age predicted psychological growth motivation, which in turn predicted both AI companion and AI assistant usage. Additionally, age also predicted motivation for emotional support, which predicted the use of AI assistants.
DOI: 10.3886/e237921v1
title: Lonely Hearts & Artificial Minds: A Survey Study on Relationships with AI Companions and AI Assistants

description: This study compares the characteristics and motivations of users of AI companions with users of AI assistants. A cross-sectional survey examined demographic factors, psychological characteristics, and motivational patterns across the two user groups through a cross-sectional survey of 230 respondents. The findings reveal distinct patterns in usage, motivations, and user characteristics between the two types of AI chatbots. Age emerged as a significant predictor with contrasting effects: older individuals showed a higher frequency of AI companion usage and lower AI assistant usage. Users of AI companions demonstrated higher levels of loneliness and lower life satisfaction, while these traits were not significant for AI assistant users. The motivational analysis demonstrated significantly higher scores for all romantic motivations to use AI companions, with the most pronounced differences among emotional support, feeling loved, care for others, and sexual satisfaction, compared to AI assistants. Motivations for psychological growth and emotional support predicted the frequency of AI companion usage. There were no significant motivational predictors for the frequency of AI assistant usage. Mediation analyses revealed that age predicted psychological growth motivation, which in turn predicted both AI companion and AI assistant usage. Additionally, age also predicted motivation for emotional support, which predicted the use of AI assistants.
DOI: 10.5281/zenodo.17116901
title: DataSet Thesis

description: This dataset is a high school academic record containing:





Student identity and background




Semester-wise grades for 12 core subjects




Final total and average scores




Information about the student's intended university and major




SNMPTN admission result (whether the student passed or failed)



This dataset is suitable for predictive modeling, especially for SNBP success prediction using machine learning algorithms such as Decision Tree, Random Forest, XGBoost, and Neural Networks.
DOI: 10.5281/zenodo.17116902
title: DataSet Thesis

description: This dataset is a high school academic record containing:





Student identity and background




Semester-wise grades for 12 core subjects




Final total and average scores




Information about the student's intended university and major




SNMPTN admission result (whether the student passed or failed)



This dataset is suitable for predictive modeling, especially for SNBP success prediction using machine learning algorithms such as Decision Tree, Random Forest, XGBoost, and Neural Networks.
DOI: 10.5281/zenodo.17073899
title: ProfhEX: Empowering Early Drug Discovery with Machine Learning-Based Target Profiling and Liability Prediction

description: The drug discovery process is inherently lengthy, complex, and costly, with high attrition rates driven by safety concerns, limited efficacy, and regulatory barriers. AI-driven computational methods have become crucial in accelerating this process by enabling the prediction of molecular activities, identification of off-target interactions, and prioritization of candidates for drug repurposing. However, existing ligand-based prediction tools often suffer from limited data coverage, narrow target scopes, and usability challenges. Here, we present an enhanced version of ProfhEX, a scalable and user-friendly platform designed for comprehensive drug-target activity profiling. The updated platform features 969 predictive models spanning 693 human targets, trained on over 5 million curated bioactivity data points. ProfhEX demonstrates high predictive accuracy in prospective real-world scenarios and surpasses state-of-the-art tools in primary target prediction benchmarks. Overall, ProfhEX represents one of the largest and most accurate platforms for compound-target prediction, supporting early-stage drug discovery and enhancing target liability assessment.
DOI: 10.5281/zenodo.17073900
title: ProfhEX: Empowering Early Drug Discovery with Machine Learning-Based Target Profiling and Liability Prediction

description: The drug discovery process is inherently lengthy, complex, and costly, with high attrition rates driven by safety concerns, limited efficacy, and regulatory barriers. AI-driven computational methods have become crucial in accelerating this process by enabling the prediction of molecular activities, identification of off-target interactions, and prioritization of candidates for drug repurposing. However, existing ligand-based prediction tools often suffer from limited data coverage, narrow target scopes, and usability challenges. Here, we present an enhanced version of ProfhEX, a scalable and user-friendly platform designed for comprehensive drug-target activity profiling. The updated platform features 969 predictive models spanning 693 human targets, trained on over 5 million curated bioactivity data points. ProfhEX demonstrates high predictive accuracy in prospective real-world scenarios and surpasses state-of-the-art tools in primary target prediction benchmarks. Overall, ProfhEX represents one of the largest and most accurate platforms for compound-target prediction, supporting early-stage drug discovery and enhancing target liability assessment.
DOI: 10.5281/zenodo.17116900
title: ТЕОРЕТИКО-МЕТОДОЛОГІЧНІ ТА ПРАКСЕОЛОГІЧНІ АСПЕКТИ РЕАЛІЗАЦІЇ ПСИХОЛОГІЧНИХ ДОСЛІДЖЕНЬ  В УМОВАХ ВОЄННОГО СТАНУ
DOI: 10.5281/zenodo.17116899
title: ТЕОРЕТИКО-МЕТОДОЛОГІЧНІ ТА ПРАКСЕОЛОГІЧНІ АСПЕКТИ РЕАЛІЗАЦІЇ ПСИХОЛОГІЧНИХ ДОСЛІДЖЕНЬ  В УМОВАХ ВОЄННОГО СТАНУ
DOI: 10.5281/zenodo.17103630
title: Data and code for manuscript: "Overground gait transitions are not sharp but involve gradually changing walk-run mixtures even over long distances"

description: Three folders are shared, containing all the code and experimental data needed to reproduce all the results in the manuscripts.

1) OvergroundFullOpt_Deterministic. Contains code for computing theenergy optima as a function of task speed.

2) FlatnessOfOptimum_SensitivityAnalysis. Contains code for computing the regions withing 1% or 2% or the energy optimum. 

3) GPSandWalkRunData. Contains all the GPS data for all trialsas well as start and stop times for bouts of walking and running for each trial for all subjects. Also contains Data+MATLAB code for walk-run fractionsand number of switches.

For the code folders, you need MATLAB to run the code or a free softwarelike Octave. The file that starts with 'root...'  can be run to produceresults.

For the data folder, the data are in the .mat format, the native MATLABdata format. These data files can be opened by MATLAB, python, or Octave.
DOI: 10.5281/zenodo.17103631
title: Data and code for manuscript: "Overground gait transitions are not sharp but involve gradually changing walk-run mixtures even over long distances"

description: Three folders are shared, containing all the code and experimental data needed to reproduce all the results in the manuscripts.

1) OvergroundFullOpt_Deterministic. Contains code for computing theenergy optima as a function of task speed.

2) FlatnessOfOptimum_SensitivityAnalysis. Contains code for computing the regions withing 1% or 2% or the energy optimum. 

3) GPSandWalkRunData. Contains all the GPS data for all trialsas well as start and stop times for bouts of walking and running for each trial for all subjects. Also contains Data+MATLAB code for walk-run fractionsand number of switches.

For the code folders, you need MATLAB to run the code or a free softwarelike Octave. The file that starts with 'root...'  can be run to produceresults.

For the data folder, the data are in the .mat format, the native MATLABdata format. These data files can be opened by MATLAB, python, or Octave.
DOI: 10.5281/zenodo.15577262
title: Data for Multilingual logs parsing

description: This is the dataset and intermediate data for paper: Beyond English Logs: An Empirical Study of Parsing Logs with Not(-only)-English Contents 

 

Translated_log_generation.zip contains the original English log datasets and the translated multilingual datasets:



original folder contains the original English datasets that contain groundtruth and Origin folder contains the original log files only.

translated_log contains the translated template file, translated log files and the groundtruth event files for multilingual datasets that are generated by our approach

translated_log_baseline contains the translated multilingual datasets that are generated by directly translated baseline.

Userstudy contains the files for user study to evaluate the accuracy of translated logs


 

Parsing_tools_evaluation.zip contains the files for evaluating the performance of existing log parsing techniques on multilingual logs:



group_check_result contains



the group accuracy checking result (groundtruth)

the group consistency checking result (ori_parsing_result)



parsing_result contains the parsing result of English datasets and mulitilingual datasets

tokenized_log contains the tokenized result for multilingual datasets


downstream_data.zip contains the files for DeepLog downstream task evaluation, each folder contains:



6 folders contains data for English, Chinese, French, Japanese, Korean, and Mix language files

For each language, the folder contains:



log_structured.csv file contains full parsing result

train.log_structured.csv file contains parsing result used for training

test.log_structured.csv file contains parsing result used for test

train and test file containing feature vectors generated by preprocess.py