cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/cnscppv7jk
title: Dataset of Digital Literacy Level Measurement of  Students, Based on the Components of Ability to use Media, Advanced use of Digital Media, Managing Digital Learning Platforms, and Ethics and Safety in the use of digital media.

description: This dataset portrays the measurement of Digital Literacy of students based on the components Ability to use Media, Advanced use of Digital Media , Managing Digital Learning Platforms , and Ethics and Safety in the use of digital media in students at the junior high school level in the Madiun area in implementing and utilizing digital technology as a means as well as a source of material for learning. 385 respondents through online survei via Google Form.
DOI: 10.17632/z75fgkvdmd
title: Three-year water isotopes of ecohydrological cycle in the CZ

description: Hydrogen and oxygen isotopes of 12 water types in the ecohydrological cycle on the CLP
DOI: 10.6084/m9.figshare.14208392.v2
title: Spatial distribution of annual and seasonal precipitation (mm) in Sierra Nevada and surroundings with the accumulated means resulting from RegRAIN interpolation.

description: From the creation of an integrated and homogenized<br>precipitation database, climatic surfaces of this variable were generated using the RegRAIN interpolation model, which in turn allowed us to examine the climatology of precipitation in Sierra Nevada. <br><br>The climatic surfaces of precipitation obtained (raster format) are represented in raster format in different time periods (annual and seasonal)<br><pre><br></pre><pre><br></pre>
DOI: 10.6084/m9.figshare.23959089.v1
title: Temperate reef structure - code and data

description: Data and code to support the manuscript entitled <i>Habitat structure shapes temperate reef assemblages across regional environmental gradients, </i>submitted to Science of the Total Environment.
DOI: 10.6084/m9.figshare.20777299.v1
title: Drone Flight Performance Evaluation Methodology based on Data Science-20220901T212610Z-001.zip

description: This dataset contains linear and angular position and velocity of a drone. Forty flights were performed by a drone pilot.
DOI: 10.17632/k5xs6rgysv
title: Data for: Identification of gas inflow zones in the COSC-1 borehole (Jämtland, central Sweden) by drilling mud gas monitoring, downhole geophysical logging and drill core analysis

description: Data_Fig_5: Drilling mud gas depth profiles of the COSC-1 borehole Data_Fig_6: Helium depth profile, DLLd, DLLs and DLLd/DLLs values, and fracture depths from core scanning
DOI: 10.6084/m9.figshare.28969778.v3
title: Processed data and statistics

description: Each sheet contains data and statistics generated for the research. Refer to the table legends bellow.<b>Supplementary Table S1. </b>Core microbiome in gut.<b>Supplementary Table S2. </b>Core microbiome in lung.<b>Supplementary Table S3.</b> Adjacency matrix of gut and lung microbiome in healthy controls.<b>Supplementary Table S4.</b> Adjacency matrix of gut and lung microbiome in patients with TB before treatment.<b>Supplementary Table S5.</b> Adjacency matrix of gut and lung microbiome in patients with TB after treatment.<b>Supplementary Table S6.</b> Node score of gut and lung microbiome in healthy controls.<b>Supplementary Table S7.</b> Node score of gut and lung microbiome in patients with TB before treatment.<b>Supplementary Table S8.</b> Node score of gut and lung microbiome in patients with TB after treatment.<b>Supplementary Table S9. </b>Significant results from differential abundance analysis of gut microbiota between HCs and patients with TB before treatment in the gut. Log fold change (logFC) represents the relative abundance in patients with TB compared to healthy controls. The logFC, p-values, and false discovery rates (FDR) were obtained from ANCOM-BC, and linear discriminant analysis (LDA) scores were calculated using LEfSe.<b>Supplementary Table S10. </b>Significant results from differential abundance analysis of gut microbiota between HCs and patients with TB before treatment in the lung. The logFC represents the log-transformed expression values of patients with TB relative to those of healthy controls (HC). The logFC, p-values, and false discovery rates (FDR) were obtained from ANCOM-BC, while the linear discriminant analysis (LDA) scores were derived from LEfSe.<b>Supplementary Table S11.</b> Significant results from differential abundance analysis of gut microbiota between patients with TB before and after treatment in the gut. The logFC represents the log-transformed expression values of patients with TB after treatment relative to those of patients with TB before treatment. The logFC, p-values, and false discovery rates (FDR) were obtained from ANCOM-BC, while the linear discriminant analysis (LDA) scores were derived from LEfSe.<b>Supplementary Table S12. </b>Significant results from differential abundance analysis of gut microbiota between patients with TB before and after treatment in the lung. The logFC represents the log-transformed expression values of patients with TB after treatment relative to those of patients with TB before treatment. The logFC, p-values, and false discovery rates (FDR) were obtained from ANCOM-BC, while the linear discriminant analysis (LDA) scores were derived from LEfSe.<b>Supplementary Tables S13. </b>Taxa clustering results in healthy controls.<b>Supplementary Tables S14. </b>Taxa clustering results in patients with TB before treatment.<b>Supplementary Tables S15. </b>Taxa clustering results in patients with TB after treatment.<br>
DOI: 10.6084/m9.figshare.7108784.v2
title: FLLA Data

description: Crystal structures dataset. Available in a Monty Encoder encoded JSON file and CSV. Recommended access method is through the matminer Python package using the datasets module.<br><br>Note on citations: If you found this dataset useful and would like to cite it in your work, please be sure to cite its original sources below rather than or in addition to this page.<br>Data representation described in the following:<br>F. Faber, A. Lindmaa, O.A. von Lilienfeld, R. Armiento, "Crystal structure representations for machine learning models of formation energies", Int. J. Quantum Chem. 115 (2015) 1094–1101. doi:10.1002/qua.24917.<br><br>Raw data sourced from the Materials Project:<br>Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G. & Persson, K. A. Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. APL Mater. 1, 11002 (2013).<br>
DOI: 10.6084/m9.figshare.909426.v2
title: Abstract1_Recommendations_Whichjournal_sorted_by_ranking.csv

description: Journal Recommendations of Whichjournal.com for the following (scrambled) Abstract: "0 0 1 10 10 2 20 20 22 4 4049 4049 4049 49 6 6 76 = = Bulk Bulk DISO DISO DISO DISO DISO Depth Dnew Dnew Dnew Dnew EvoCeram Fill Fill Filtek Filtek Filtek For For ISO ISO On Plus Quixfil SDR Silorane Silorane Supreme Surefil Tetric The To VHN VHN Venus Vickers Vickers Vickers Vickers a a according accurately all and and and and and and and and at at at basis been beginning between between bulk bulk bulk bulk by by by closest compared compared composite composite composite composites composites control cure cure cure cure cure“ defined depth depth depth depth determined determined determined determined device distances e e each either either evaluate except fill fill fill fill filled for for for for four generated group group group had hardness hardness hardness hardness highest i i if indentation is light-cured light-cured light-curing made materials materials materials materials materials measurements measurements method method microhardness microhardness mm mm mold mold n n of of of of of of of of one or or overestimated per per placed prepared proceeding profiles profiles profiles reflected resin resin resin resin resin s s s s showing six smaller specimens than that the the the the the the the the the the the the to to to to toward toward two uncured under unit varied varied was was was was were were were when with with with “ISO “bottom“ “top“
DOI: 10.17632/778yzh2cv4
title: Are methane emissions from mangrove stems a cryptic carbon loss pathway? Insights from a catastrophic forest mortality.

description: Here we provide the raw data from our publication in New Phytologist (accepted 10 June 2019). Out study highlights are as follows: • Growing evidence indicates tree-stem methane emissions may be an important and un-accounted for component of local, regional and global carbon budgets. Studies to date have focussed on upland and freshwater swamp-forests; however, no data on tree-stem fluxes from estuarine species currently exist. • Here we provide the first-ever mangrove tree-stem methane flux measurements from &gt;50 trees (n=230 measurements), in both standing dead and living forest, from a region suffering a recent large-scale climate-driven dieback event (Gulf of Carpentaria, Australia). • Average methane emissions from standing dead mangrove tree-stems were 249.2 ± 41.0 µmol m-2 d-1 and eight-fold higher than from living mangrove tree-stems (37.5 ± 5.8 µmol m-2 d-1). The average methane flux from tree-stem bases (~10cm above ground) was 1071.1 ± 210.4 µmol m-2 d-1 and 96.8 ± 27.7 µmol m-2 d-1 from dead and living stands respectively. Sediment methane fluxes and redox potentials did not differ significantly between living and dead stands. Our results suggest both dead and living tree-stems act as methane conduits to the atmosphere, bypassing potential sedimentary oxidation processes. • Although large uncertainties exist when up-scaling data from small-scale temporal measurements, we estimated that dead mangrove tree-stem emissions may account for ~26 % of the net ecosystem methane flux.
DOI: 10.17632/dzkkv87njb
title: Data for: Equivalent Circuit Model Recognition of Electrochemical Impedance Spectroscopy via Machine Learning

description: 1. EIS-Data.csv: The original EIS is extracted from the literature and discretized into readable data points. All these EIS come from electrochemical energy storage devices, especially lithium-ion batteries and supercapacitors. 2. Test-Data-Prob.csv: The probability for each model type samples in test data.
DOI: 10.6084/m9.figshare.28016057.v1
title: This is a companion dataset to the paper "Precipitation Variability in CMIP6 Climate Models across the North Atlantic–European region and their Links to Atmospheric Circulation" by E. Plavcová submitted to Climate Dynamics.

description: Long-term changes in climate variability represent an important aspect of climate change, with various impacts on society and environment. In this study, we analyze outputs from 13 CMIP6 global climate models (GCMs) across the North Atlantic–European domain, focusing on their simulations of precipitation probability and short-term variability in both historical and future climates. Precipitation probability denotes the probability of a wet day (&gt; 1 mm), and precipitation variability reflects the tendency to cluster wet days into sequences. By comparing against the ERA5 reanalysis, we found that the GCMs tend to overestimate precipitation probability across Europe in winter, whereas in summer, they have a tendency to underestimate it around 50°N. Precipitation variability is, on average, underestimated by the GCMs in summer, while overestimated in several regions in winter. Projections for the end of the 21st century indicate significant changes in both precipitation probability and variability which are more pronounced under the more pessimistic emission scenario compared to the moderate one. We found that the changes in probability and variability are mutually independent: the former being more latitudinal-dependent while the latter differs between the west and east. After identifying atmospheric circulation conducive and non-conducive to precipitation occurrence, we found that GCMs overestimating the frequency of conducive circulation tend to overestimate precipitation probability, and vice versa. Furthermore, increased precipitation variability is associated with higher circulation variability. Finally, our analysis reveals that projected changes in precipitation probability and variability are often linked to projected changes in atmospheric circulation, especially in winter.
DOI: 10.6084/m9.figshare.3567060.v1
title: Appendix A. Gaussian random field priors and precision hyper-priors.

description: Gaussian random field priors and precision hyper-priors.
DOI: 10.17632/zcjbk69nj6
title: Lead isotope ratios for Bronze Age and Early Iron Age metal artifacts from south-west Poland

description: The lead isotope ratios for Bronze Age and Early Iron Age metal artifacts from the south-western Poland. Data were used in:
Nowak K, Stolarczyk T, Stos-Gale Z, Baron J, Derkowska K, Derkowski P, Miazga B, Woodhead J, Karasiński J and Maas R (2023), Question of local exploitation of copper ore deposits in the Urnfield time in Poland. Front. Earth Sci. 11:1184949. doi: 10.3389/feart.2023.1184949
DOI: 10.6084/m9.figshare.21225413.v3
title: Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability Detection

description: Data package for "Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability Detection", published in ICSE 2024, with updates from Artifact Evaluation.Paper link: https://www.computer.org/csdl/proceedings-article/icse/2024/021700a166/1RLIWqviwEMSee Github repo for updates: https://github.com/ISU-PAAL/DeepDFAData dictionary:before.zip: CFGs of Big-Vul dataset, generated by Joern.preprocessed_data.zip: preprocessed data from Big-Vul for running DeepDFA, including preprocessed Joern CFGs and abstract dataflow embeddings.DeepDFA-code.zip: most recent version of the code as of the publication of this artifact, see Github repo for updates: https://github.com/ISU-PAAL/DeepDFAMSR_data_cleaned.csv: original Big-Vul dataset, see original source: https://github.com/ZeoVan/MSR_20_Code_vulnerability_CSV_DatasetMSR_LineVul: LineVul's preprocessed version of the Big-Vul dataset, see original source: https://github.com/awsm-research/LineVulChangelog:v1 2023-09-20: original data package and Github repo published.v2 2024-01-04: added full instructions and bug fixes for Artifact Evaluation.v3 2024-01-10: integrated feedback from Artifact Evaluation.
DOI: 10.17632/fz89mk3232
title: Inclusión educativa y enseñanza de lenguas extranjeras a alumnos con discapacidad visual: percepciones y experiencia de profesionales paraguayos

description: Percepciones de 27 profesionales que trabajan con aprendices con discapacidad visual, sobre: principales barreras para la inclusión educativa de ese alumnado en Paraguay; y conocimiento y uso de metodologías, recursos, técnicas y actividades generales de educación y de acceso al aprendizaje de lenguas extranjeras. Para ello, se realizó una encuesta en tres instituciones de atención educativa especializada, ubicadas en Asunción. Entre las barreras señaladas por estos profesionales, se destacaron: el rechazo, desinterés y la falta de capacitación de profesores regulares; la escasez de recursos de las escuelas, así como la dificultad en encontrar y obtener esos recursos; la falta de apoyo o sobreprotección de las familias; y la asistencia irregular a las instituciones especializadas. En lo que se refiere a la enseñanza de lenguas extranjeras, tres de los participantes indicaron tener experiencia enseñando la materia, y revelaron desconocer metodologías específicas de instrucción para el público con deficiencia visual. Las dificultades encontradas por ellos versaban sobre el acceso a recursos de instrucción adaptados en el país, y la evaluación de la expresión y comprensión escrita de los aprendices en la lengua. Entre las técnicas y actividades de instrucción, los participantes apuntaron a la importancia del uso de enfoques vivenciales, la descripción de elementos visuales y el trabajo con diversos tipos de texto, como siendo las más eficientes. También se mencionaron el uso de otros sentidos, trabajar con textos en formato audio, el trabajo en equipo, escribir textos, y trabajar con objetos concretos. Los datos fueron compilados en gráficos y narrativas, para ayudar en el análisis de los mismos. Cada carpeta contiene sub secciones, pues se separaron las preguntas abiertas por un lado, y las cerradas, de opción múltiple y escala Likert, por otro. Los datos brutos y sus codificaciones, cuanto a ellos, se encuentran disponibles en formato EXCEL en la carpeta DATOS COMPILADOS. En el mismo archivo, también están reunidas las preguntas del cuestionario aplicado.
DOI: 10.6084/m9.figshare.11604588.v1
title: FSII (2020 version) - Continent

description: The collection contains Forest Structural Integrity Index data for the Tropical and Subtropical Moist Broadleaf Forests Biome. The collection is updated from the data provided in Hansen, A. et al. Global humid tropics forest structural condition and forest structural integrity maps. Scientific Data 6, 232, doi:10.1038/s41597-019-0214-3 (2019). Loss year extends to 2018. The spatial extent was expanded to the full biome.
DOI: 10.6084/m9.figshare.21652007.v2
title: CBDB

description: Comprehensive understanding of the molecular composition in Chinese baijiu is of a great value for understanding the flavor and improving the quality. Liquid chromatography-mass spectrometry (LC-MS) is a good tool for the analysis of Chinese baijiu. However, lack of the related database makes corresponding LC-MS data difficult to be annotated. Therefore, we constructed a Chinese baijiu compound database (CBDB) containing 5709 compounds based on literatures and text mining, and further a Chinese baijiu LC-MS database (LCMS-CBDB) was built by endowing each compound with retention time, exact mass, and MS/MS spectrum. In total 468 compounds were identified in six Chinese baijiu samples by searching LCMS-CBDB. Further, database enhancement strategies were proposed based on biological transformations and theoretical homologue expansion, which allows the identification of new compounds. Five monoglycerides were successfully identified and validated using the extended CBDB, four of them were reported in baijiu for the first time.
DOI: 10.6084/m9.figshare.21780508.v1
title: Robinson_et.al_procb_ESM_Tables S7-S11 from Macroevolution of protective coloration across caterpillars reflects relationships with host plants

description: Information related to construction of phylogenetic tree
DOI: 10.6084/m9.figshare.20459784.v2
title: URDEdataset

description: This data set can be used to train machine learning models for dust identification and segmentation. It includes road dust images, hand annotated images of dust cloud, segmentation masks and dust particle mesurements relavant to selected images.
DOI: 10.6084/m9.figshare.28122392.v1
title: 24B02428832__(Z241218-0321)FANCD2_ex16_2R__F11_2412180006G.ab1

description: 24B02428832__(Z241218-0321)FANCD2_ex16_2R__F11_2412180006G.ab1
DOI: 10.6084/m9.figshare.26793622.v3
title: A multicenter dataset for lymph node clinical target volume delineation of nasopharyngeal carcinoma

description: We retrospectively collected 440 computed tomography (CT) images from 262 patients with different imaging modalities, disease stages and treatment strategies. To protect the patients' private information, we first blurred the face region and removed all metadata about clinical and personal. Then, all files are stored with the format NIfTI-1 (32-bit floating point). Each cohort is stored as a folder and named "Internal\_Cohort", and "Testing\_Cohort\_X" ("X" is from 1 to 4), Raw CT images in the training or testing sets are stored in the "imagesTr" or "imagesTs" folders of each cohort with the name "lnctvseg\_cx\_xxxx\_yyyy.nii.gz", respectively, where "cx" denotes the center number x is the digit number from 1 to 4, "xxxx" is the index number of each patient the range is 1 to the total number of patients in "cx", "yyyy" is set to "0000" (non-contrast CT) or "0001" (contrast-enhanced CT). The corresponding labels are stored in the "labelsTr" and "labelsTs" folders with the name "lnctvseg\_cx\_xxxx.nii.gz", respectively.
DOI: 10.6084/m9.figshare.16809982.v1
title: Data for LiuTanWu_JAS_2022

description: This RAR file includes initial, lateral, and surface boundary condition files for cloud-permitting simulation with WRF model. Namelist settings are also included.<br>Details of other model settings can be found in the paper submitted to Journal of the Atmospheric Sciences by Yan Liu, Zhe-Min Tan, and Zhaohua Wu. <br>We welcome any comments and questions, which can be sent to yliu90@nju.edu.cn.<br>
DOI: 10.6084/m9.figshare.28583252.v1
title: genomic_data_DED_DNB_SNC_Hessenauer2025

description: Climate significantly influences the distribution, composition, and diversity of fungal communities, impacting the growth, spread, and virulence of fungal forest pathogens. This study employs advanced landscape genomics methods to explore the genomic adaptations of three major fungal pathogens: those responsible for Dutch elm disease, dothistroma needle blight, and Swiss needle cast. Our findings reveal that precipitation and humidity are primary drivers of adaptation in these species. We use these insights to forecast potential adaptations under future climate scenarios (genomic offset) and identify specific genes and pathways associated with climate responses in each pathogen. Notably, we detect a convergence in moisture adaptation across these distantly related species, particularly in genes related to the cytoskeleton and transporters. This study enhances our understanding of fungal pathogen evolution in response to climate change, offering crucial insights for forest disease management.This dataset includes filtered genomic (vcf format), climate (csv format) and spatial (csv) data for all three pathogens considered in the study (Dutch elm disease = DED, dothistroma needle blight = DNB and swiss needle cast = SNC), please refer to Hessenauer et al. 2025 for details.