cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/ck8x2xdwby
title: Leaf and ecosystem water use effciencies differ in their global-scale patterns and drivers

description: Water use effciency (WUE) links carbon and water cycling and has been recognized as important in understanding the carbon-water budget of terrestrial ecosystems. However, there are few studies comparing WUE at leaf and ecosystem levels in response to environmental variables on a global scale. Here, we compare global-scale patterns and the drivers of leaf and ecosystem WUEs and quantify the relative inﬂuence of biotic and abiotic factors. Using published world-wide δ13C (carbon stable isotope composition) measurements for 6751 C3 plant populations from 174 publications, as well as our own measurements of δ13C for 418 C3 plant populations across drylands in China, and satellite-based datasets of gross primary production and evapotranspiration, we determined global patterns and the drivers of leaf and ecosystem WUEs. Leaf intrinsic WUE (iWUE) and ecosystem WUE displayed almost opposite trends, in response to abiotic factors on a global scale. iWUE was highest in arid
regions and lowest in humid regions, whereas ecosystem WUE was lowest in arid regions and highest in humid regions. Phylogeny had a signifcant effect on iWUE. Mean annual temperature (MAT) was the strongest factor in predicting iWUE, whereas the most robust factor in predicting ecosystem WUE was leaf area index (LAI). The data indicate that the two different responses at the leaf and ecosystem levels must be considered when modeling carbon and water balances in response to climate change.
DOI: 10.17632/58cstgmrg9
title: FEM Models for Pneumatic Actuation of Soft Robots

description: This dataset contains two finite element (FEM) simulation codes for analyzing pneumatic actuation in soft robotic structures:

1) A reference hyperelastic model simulating large deformations under internal pressure using a body fitted mesh.
2) A porohyperelastic model implementing fluid-solid interactions by means of permeability and stiffness interpolation in a fictitious domain.

Both implementations use the GetFEM framework (in Python) and model a two-chamber pneumatic system undergoing large deformations under pressurization of one of the chambers.
DOI: 10.17632/2t4b33ftst
title: Bud phenology data

description: This dataset contains phenological observations (stored as numbers from 0-6) of white spruce (3 seed sources), black spruce (1 seed source) and Norway spruce (4 seed sources) buds in spring gathered over 2 years (2016-2017) on trees growing in two plantations that were established in two different blioclimate zones in eastern Canada (the northern temperate forest and the boreal mixedwood forest). For each tree, the terminal bud of all branches belonging to the whorl at breast height were observed weekly. We identified inter- and intraspecies variation in bud break timing and in sensitivity to environmental cues (frost probability, GDD, photoperiod) for each phenological stage. We found that spring frosts and photoperiod control the firsts phenological stages and GDD controls the lasts phenological stages which highlights an important ecological trade-off between the maximizing the growing season length and limit frost damage from too early bud burst.
DOI: 10.17632/bj5vtsj7bj
title: Data for learning and motivation in H E

description: This study explores the relationship between motivational and learning strategies and academic performance among university students in Romania, an area that remains underexplored, particularly in relation to academic performance across different study programs. Data were collected from students enrolled in full-time and hybrid study programs in the field of educational science (N = 225). The results reveal statistically significant findings, with a model precision of 58.7%. While the holistic Motivation Scale and Learning Strategy Scale did not significantly impact academic performance, specific subscales emerged as influential predictors. Key motivational strategies, such as task value, control of learning beliefs, self-efficacy, and learning strategies like effort regulation, metacognitive self-regulation, and help-seeking, were found to impact academic performance significantly. Additionally, the frequency of study programs (full-time vs. hybrid) emerged as a significant moderator, particularly for lower-performing students. These findings highlight the importance of fostering students' beliefs in their learning abilities, their perceived control over learning outcomes, and their valuation of academic tasks. The study underscores the need for targeted interventions to enhance students' motivation and self-regulation, ultimately improving academic performance. These insights offer valuable guidance for educators and policymakers aiming to design effective support systems for students in higher education.
DOI: 10.17632/584ydjx3xm
title: Engineered Cementitious Composites Dataset / Strain Hardening Cementitious Composites Data for FDNN ensemble predictive model

description: The data is for the research titled "An Ensemble Method for Predicting the Mechanical Properties of Strain Hardening Cementitious Composites" in CONSTRUCTION AND BUILDING MATERIALS journal. The folder "Models" Contains the models in the project organized as follows: * "Comparison Models" is the folder Containing the models currently available for comparison with our FDNN. * "FDNN Ensemble" Folder contains the neural network models for the FDNN ensemble * "FRST" Folder contains the random forest regressors component of the FDNN ensemble Two ".xlsx" files are provided in this project. * "Full SHCC Dataset.xlsx" is the Raw dataset collected from literature * "sep_dataset.xlsx" is the processed dataset used for training the models Five other files are provided in this project, as: * "Data description.txt" contains a small description of the data * "FDNN Training.ipynb" contains the FDNN training algorithm * "FDNN predictor.ipynb" contains a small demo of how to use the FDNN models to predict the properties of SHCC * "normalization_coefficiants.py" contains the normalization values that can be used to encode and decode the inputs for prediction purposes * "predict SHCC mix.py" is a python implementation of the "FDNN predictor.ipynb" file * "sep_dataset.xlsx" is the processed dataset used for training the models Finally a Description of the parameters is given in the file "Data description.txt" To develop the model, data were collected from a comprehensive literature review of articles. The dataset consists of several parameters related to the mix design and mechanical properties of SHCC. The mix design parameters included in the database are the amount of cement, water, fine aggregate, fly ash, silica fume, cenosphere, blast furnace slag, superplasticiser, coarse aggregate, accelerating admixtures, phase change materials, fibre weight, all represented in percentage weight. The parameters related to fibre properties in the dataset are fibre type, volume (% volume), diameter (Micro-meter), length (mm), tensile strength (MPa), and elastic modulus (GPa). The dataset also included rubber, light weight aggregate, viscosity agent hydroxypropyl methylcellulose, Air entraining admixture, oiling agent, graphene oxide, and defoamer represented in binary forms, i.e. 1 (was used in mix design) or 0 (was not used). Other experimental conditions are included, they are water curing time (days), air curing time (days), high frequency & velocity casting (1 or 0), and temperature (⸰C). Finally, the mechanical properties recorded in the dataset are the tensile stress at the first crack (MPa), tensile strain at the first crack (%), peak tensile stress (MPa), peak tensile strain (%), flexural strength at the first crack (MPa), flexural strain at the first crack (%), peak flexural Strength (MPa), peak flexural strain (%), and Compressive strength (MPa). In total, the resulting data set contains 38 parameters, divided into 29 features and nine targets.
DOI: 10.17632/7rbhv3d2h8
title: Data for Thesis 

description: FINAL_DATASET contains the food items consumed in the UK in the year 2019, along with the nutritional values of each food item. SUB_DATASET contains all the data from FINAL_DATASET with an added row of mycoprotein data.
DOI: 10.17632/trh2w8pzc7
title: Role of Perp-mediated apoptosis in thymic negative selection of pathologic CD4+ T lymphocytes

description: presentative facs raw data and SourceData of Western Blot
DOI: 10.17632/whk6wdy3nn
title: PNADIS: An automated Peierls–Nabarro analyzer for dislocation core structure and slip resistance

description: Dislocation is one of the most critical and fundamental crystal defects that dominate the mechanical behavior of crystalline solids, however, a quantitative determination of its character and property in experiments is quite challenging and limited so far. In this paper, a fully automated Peierls-Nabarro (P-N) analyzer named PNADIS is presented; a complete set of the character and property of dislocation can be automatically derived, including the dislocation core structure, Peierls energy and stress, pressure field around dislocation core, solute/dislocation interaction energy, as well as the energy barrier and increase in critical-resolved shear stress at 0K for solid solution strengthening. Furthermore, both one-dimensional (1D) and two-dimensional (2D) P-N models are implemented to meet the demand to analyze the character and property of dislocation for not only simple FCC and HCP structures but also complex crystals. The implementation of this code has been critically validated by a lot of evaluations and tests including 1D P-N model for complex crystals, 2D P-N model for FCC and HCP metals, pressure field around dislocation core, and solid solution strengthening for alloys. We expect that the automated feature of this code would provide a high-efficiency solution for determining the character and property of dislocation.
DOI: 10.17632/jjzrpr659v
title: Dataset Brazilian TEIQue-SF

description: Brazilian dataset. Brazilian-Portuguese-TEIQue-SF
DOI: 10.17632/pfp7zmxcyx
title: Civil Society Active in Corporate Accountability Dataset

description: The present dataset is the first one to provide detailed information on civil society organizations engaged in an emerging field of civil society activity and of scholarship: corporate accountability. The rationale behind generating this dataset lies on the fact that there was little data driven information on the activities of (global) civil society in holding corporations accountable. Available data focused, so far, on the big Western-based NGOs and was mainly oriented toward case studies. As such, the dataset is the first source providing quantitative data on the pluriverse of organizations active in the field of corporate accountability. It includes 42 variables, grouped into six categories. The data covers both structural/organizational information (such as organizational features, number of employees, etc.) and information pertaining to the more subjective dimensions of activities (such as aims, values, and blame attribution), and allows for systematic and cross-national analyses. Further, if provides detailed information on the activities and repertoires carried out and employed by organizations. The classifications are based on common practices in the use of AOE and PEA. It also includes a wide range of network variables (e.g. partner organizations) and information on funding (sources of funding, annual budget, etc.). It can, therefore, be used for a variety of analysis, including but not limited to studying the interaction between funding and activities, location and political leaning, repertoires of action and values, etc. The coding was based on information provided on the organizations’ websites (e.g., “What we do” sections) and in annual reports. Both current and past activities (if mentioned) were coded. The data was gathered between December 2022 and April 2023. So far, the dataset has been used to study the effect of donor dependency on civil society. We found that official funding impedes contentious repertoires and severely negatively impacts on the willingness of organizations to aim for a reform of the economic system.
DOI: 10.17632/rg7db6xyh7
title: Anselmetetal_2025
DOI: 10.17632/rwznmmbxzz
title: Guo et al. A modified laser ablation-isotope ratio mass spectrometry method for in situ analysis of sulfur isotope composition of sulfides

description: data from Guo et al. RCM 2024 entitled "A modified laser ablation-isotope ratio mass spectrometry method for in situ analysis of sulfur isotope composition of sulfides".
DOI: 10.17632/2pxhn7f35n
title: Temperature string device (TSD): a new low-cost instrument for sea ice observations in remote areas

description: Manufacturing and operation of the Temperature String Device (TSD)
DOI: 10.17632/93ngk2rn74
title: Data for: Insight into the mechanism of action of EP-39, a bevirimat derivative that inhibits HIV-1 maturation

description: In silico docking of BVM and EP-39 on the hexameric crystal structure of the CACTD-SP1 Gag fragment
DOI: 10.17632/znnccm8nr9
title: 0000-0002-6547-3541

description: Media Attention, Corporate Stock Price Synchronicity and Crash Risk
DOI: 10.17632/gk2jgs9vh9
title: In-season Corn Yield Prediction Using Satellite-derived Solar-Induced Chlorophyll Fluorescence and Machine Learning Algorithms data

description: This data is for the U.S. Corn Belt (US-CB), a major agricultural region characterized by intensive corn cultivation. The data includes 210 counties consistently dominated by corn production, selected based on their suitability for sub-pixel SIF disaggregation and inclusion in the USDA’s annual yield reporting. The data include five growing seasons: 2015, 2016, 2018, 2019, and 2020. The year 2017 was excluded from the data due to incomplete satellite data caused by sensor malfunction.
Annual corn yield records were obtained from the U.S. Department of Agriculture’s National Agricultural Statistics Service (USDA-NASS). County-level yield data, measured in tons per hectare, are generated using a combination of farmer-reported surveys, field assessments, and calibrated simulation models. This integration ensures their reliability as ground-truth references for both training and validating predictive models. Their consistent spatial resolution and broad temporal span across the U.S. Corn Belt make them a solid basis for model development and performance testing.
Standard satellite-derived SIF products are provided at coarse spatial resolutions, complicating their use in heterogeneous agricultural landscapes. To overcome this limitation, I applied a sub-pixel extraction method developed by Kira and Sun (2020), which uses high-resolution land cover and crop maps to isolate the contribution of corn within each SIF pixel. This technique allowed for the derivation of corn-specific SIF values at the county level, minimizing contamination from other crops or land covers.
DOI: 10.17632/gmmyws36h3
title: Dataset of Pressure Injury Prevention Knowledge, Attitude and Practice of Nurses at Sabah, Malaysia

description: This dataset describes nurses' knowledge, attitude, and practices (KAP) of pressure injury prevention at public and private hospitals in the West Coast division of Sabah, Malaysia. It was collected using a structured questionnaire administered online to 448 nurses. It includes nurses working at these hospitals and was conducted from April to December 2021. This dataset includes the (bilingual) Bahasa Malaysia and English translations of the questionnaire that had been used for the survey.
DOI: 10.17632/j3z5bmvmnd
title: GC-EI-MS datasets of trimethylsilyl (TMS) and tert-butyl dimethylsilyl (TBDMS) derivatives for development of machine learning-based compound identification approaches

description: In the field of environment and health studies, recent trends have focused on the identification of contaminants of emerging concern (CECs). This is a complex, challenging task, as compound databases (DBs) and mass spectral libraries (MSLs) concerning these compounds are very poor. This is particularly true for semi-polar organic contaminants that have to be derivatized prior to gas chromatography-mass spectrometry (GC-MS) analysis with electron impact ionization (EI), for which it is barely possible to find any records. In particular, there is a severe lack of datasets of GC-EI-MS spectra generated and made publicly available for the purpose of development, validation and performance evaluation of cheminformatics-assisted compound structure identification (CSI) approaches, including novel cutting-edge machine learning (ML) approaches.
In order to fill in this gap, we have generated 12 datasets of GC-EI-MS spectra of trimethylsilyl (TMS) and tert-butyldimethylsilyl (TBDMS) derivatives, which can be used to support machine learning-assisted CSI and to aid in cheminformatics-assisted identification of silylated derivatives in GC-MS laboratories working in the field of environment and health. The dataset includes:
- Four test datasets of raw (RAW) and processed (BS) GC-EI-MS spectra of TMS and TBDMS derivatives of CECs, given as .txt, .mgf and .msp files;
- Two sets of corresponding metadata (for TMS and TBDMS derivatives), which contain the IUPAC name, exact mass, molecular formula, InChI, InChIKey, SMILES and PubChemID of each CEC and the corresponding CEC-TMS or CEC-TBDMS derivative, where available;
- Metadata for four datasets of GC-EI-MS spectra of TMS derivatives derived from the NIST 17 Mass Spectral Library, including an initial dataset selected from the library and three datasets generated by applying consecutive filtering approaches. These metadata files contain common name, InChIKey, molecular formula, CAS number, exact mass, molecular weight, NIST number and ID of the GC-EI-MS spectra. TMS derivatives_0.1 refers to the original dataset derived from the NIST 17 Mass Spectral Library, TMS derivatives_1.1 to the dataset resulting after a first filtering step, TMS derivatives_2.1 after second and TMS derivatives_3.1 after third and final filtering step; and
- Metadata for four datasets of GC-EI-MS spectra of TBDMS derivatives derived from the NIST 17 Mass Spectral Library, including an initial dataset selected from the library and three datasets generated by applying three consecutive filtering steps. These metadata files contain the name, InChIKey, molecular formula, CAS number, exact mass, molecular weight, the NIST number and the ID of the GC-EI-MS spectra. TBDMS derivatives_0.1 refers to the original dataset derived from the NIST 17 Mass Spectral Library, TBDMS derivative_1.1 to the dataset resulting after a first filtering step, TBDMS derivative_2.1 after second and TBDMS_3.1 after third and final filtering step.
DOI: 10.17632/zy9kw7p78g
title: Nucleation of twin boundaries from &lt;110&gt; asymmetric tilt grain boundaries in BCC-Fe

description: The following four video files have been included with the manuscript “ Nucleation of twin boundaries from &lt;110&gt; asymmetric tilt grain boundaries in BCC-Fe”.
J. Veerababu, A. Nagesha, and vani Shankar
DOI: 10.17632/k46nr3wnnj
title: Stacked Seismic Ambient Noise Cross-correlations between 860 Station Pairs in the Makran Subduction Zone

description: Stacked cross-correlations (CC) between 860 station pairs, bandpass filtered between 2 and 70 seconds, were calculated following the routine described by Bensen et al. (2007). The CCs are provided in SAC format (directory: "Stacked-cross-correlations-in-SAC"), and the extracted group velocity dispersion curves are presented in text format (directory: "Dispersion-Curves-in-Text"). Additionally, we shared the regional earthquakes used for extracting the Rayleigh wave group velocity dispersion curve in SAC format (directory: "Regional-earthquakes-in-SAC"). These data have been uploaded to be shared through a manuscript by Mehrdar et al., submitted to Geophysical Journal International.

Reference:
Bensen, G.D., Ritzwoller, M.H., Barmin, M.P., Levshin, A.L., Lin, F., Moschetti, M.P., Shapiro, N.M., et al., 2007. Processing seismic ambient noise data to obtain reliable broad-band surface wave dispersion measurements. Geophys J Int, 169, 1239–1260. doi:10.1111/j.1365-246X.2007.03374.x
DOI: 10.17632/g2jbykk3vm
title: X-X-X-X (2025) 2.4.3.10 (Anonymized).xlsm

description: This section is an Excel file titled "X-X-X-X (2025) 2.4.3.10 (Anonymized).xlsm," which includes the Web of Science (WOS) downloaded data, process-related data, cleaned (formatted) data, and VBA codes (Note: The VBA codes are compatible exclusively with Excel on Windows).
DOI: 10.17632/pngmgm9yr3
title: MC-4C pipeline: test data

description: This is a reduced MC-4C dataset provided to be used as a test case for running MC-4C pipeline. The MC-4C pipeline can be freely downloaded from: https://github.com/aallahyar/mc4c_py Changes compared to original upload: - Changed name of fastq file from "raw_BMaj-test.fastq" to "fq_BMaj-test.fastq.gz" - Fields are now separated by comma instead of semi-colon
DOI: 10.17632/c24g4md953
title: Automatic measurement of hallux valgus angle

description: This is the dataset for the article, 'Automatic estimation of hallux valgus angle using deep neural network with axis-based annotation '. (Takeda et.al., Skeletal Radiology 2024)
https://link.springer.com/article/10.1007/s00256-024-04618-2
Cite this paper when you use this newral network model for research purposes.

This dataset contains the developed deep neural network model, automatic HVA measurement application utilizing the neural network,
 and statistical analysis code used for the validation cohort to evaluate the accuracy of the neural network model.

This dataset does not include the patient's radiographs because of the privacy concerns.
DOI: 10.17632/6tddv74p8k
title: [Dataset] Physiochemical characterization of fabricated starch-based nanoscaffolds incorporated with Pocockiella variegata extract-mediated green synthesised copper oxide nanoparticles

description: This dataset presents a comprehensive physicochemical characterization of starch-based nanoscaffolds integrated with copper oxide nanoparticles (CuO NPs) synthesized via a green approach using Pocockiella variegata algal extract. The study focuses on evaluating the structural, morphological, and functional properties of these nanocomposite scaffolds, which combine biodegradable starch polymers with eco-friendly CuO NPs for potential biomedical applications. Key analytical techniques include scanning (SEM) for assessing nanoscaffold porosity and nanoparticle dispersion, X-ray diffraction (XRD) for crystallinity analysis of CuO NPs, and Fourier-transform infrared spectroscopy (FTIR) to identify chemical interactions between starch matrices and nanoparticles. Additional data on thermal stability (via TGA) are provided to assess scaffold durability. By merging natural polymer scaffolds with green-synthesized nanoparticles, this work highlights sustainable strategies for developing advanced biomaterials. The findings are relevant to tissue engineering, wound dressings, or drug delivery systems, emphasizing eco-friendly alternatives to conventional synthetic nanomaterials. Researchers can leverage this dataset to optimize nanoparticle loading, scaffold architecture, and functional performance for targeted biomedical uses.