cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24117144.v1
title: A dataset on the hydrodynamic force on individual salt marsh plant and the flow structure and wave dessipation over a meadow of plants under waves with and without current

description: This dataset includes laboratory measurements of both the force on individual salt marsh plants and the flow structure and wave energy dissipation over a meadow of plants. Both experiments were conducted in the Nepf Fluid Mechanics lab at MIT in a 24-m-long, 38-cm-wide, 60-cm-tall water channel. Data measured in the individual plant experiment (IE) was represented as synchronized measurements of plant drag and free surface displacement, as well as 3-dimensional velocity profiles provided as raw data, phase-averaged data, and statistical data. The meadow experiments (ME) provide time-varying data of free surface displacement along the meadow at 10 and 15 cm intervals, as well as velocity profiles upstream of and within the meadow with 1 to 2 cm vertical resolution. In addition, the turbulent kinetic energy for each measurements and the wave decay coefficients for all conditions were calculated and reported.
DOI: 10.6084/m9.figshare.24124641
title: Supplementary Material for: Trends in accuracy and appropriateness of alopecia areata information obtained from a popular online large language model, ChatGPT

description: Patients with alopecia areata (AA) may access a wide range of sources for information about AA, including the recently developed ChatGPT. Assessing the quality of health information provided by these sources is crucial, as patients are utilizing them in increasing numbers. This study aimed to evaluate appropriateness and accuracy of responses to common patient questions about AA generated by ChatGPT. Responses generated by ChatGPT 3.5 and ChatGPT 4.0 to 25 questions addressing common patient concerns were assessed by multiple attending dermatologists in an academic center for appropriateness and accuracy. Appropriateness of responses by both models for use in two hypothetical contexts: 1) for patient-facing general information websites, and 2) for electronic health record (EHR) message drafts were measured. This study found the accuracy across all responses was 4.41 out of 5. Accuracy scores of responses ChatGPT 3.5 responses had a mean of 4.29, whereas those generated by ChatGPT 4.0 had mean accuracy score of 4.53. Assessments ranged from 100% of responses rated as appropriate for the general question category to 79% questions about management for an EHR message draft. Raters largely preferred responses generated by ChatGPT 4.0 vs. ChatGPT 3.5. Reviewer agreement was found to be moderate across all questions, with a 53.7% agreement and Fleiss’ κ co-efficient of 0.522 (p-value &lt; 0.001). The large language model ChatGPT outputted mostly appropriate information for common patient concerns. While not all responses were accurate, the trend toward improvement with newer iterations suggests potential future utility for patients and dermatologists.
DOI: 10.6084/m9.figshare.24124425
title: Data files

description: Data of phytocomponuds present in plant extract by GC-MSWord files (2-6) are bioinformatics analysis dataExcel sheet of SRB assay data
DOI: 10.21227/grxc-xb59
title: Carbon Footprint Optimisation of dishes (HashMap AIA and Apriori Version)

description: The dataset aims to facilitate research in the optimization of the carbon footprint of recipes. Consisting of 30 Excel files processed through various Python scripts and Jupyter notebooks, the dataset serves as a versatile resource for both performance analysis and environmental impact assessment. The unique attribute of this dataset lies in its ability to calculate representative values of carbon footprint optimization through multiple algorithmic implementations. This enables the research community to understand the trade-offs and benefits associated with different optimization strategies. It should be noted that the dataset is specifically configured for the initial set of data based on "Courtepaille" recipes. Users intending to employ different datasets must make appropriate code modifications to ensure correct execution. Therefore, potential limitations exist in terms of the extensibility of the dataset for different types of culinary data. While associated publications are not yet available, they are forthcoming, underscoring the dataset's value in advancing the domain of carbon footprint optimization in food recipes.
DOI: 10.6084/m9.figshare.24124821
title: Supplementary Material for: Renal outcomes in older adults with antineutrophil cytoplasmic autoantibody-associated vasculitis: a new prediction model

description: Introduction: Older patients with antineutrophil cytoplasmic autoantibody-associated vasculitis (AAV) commonly experience renal impairment and poor prognoses. This study aimed to establish a risk-scoring system for predicting composite renal outcomes in older patients with AAV. Methods: This retrospective observational study included all patients with AAV hospitalized in a single-center tertiary hospital in China between January 2013 and April 2022. Patients aged ≥ 65 years were defined as older adults, and short-term composite renal outcomes included a ≥ 25% reduction in eGFR (for AKI), renal replacement therapy, provision of renal replacement therapy (long-term dialysis, kidney transplant, or sustained eGFR &lt; 15 ml/min/1.73m), or all-cause mortality. Patients were randomly divided into development and validation cohorts (2:1). Logistic regression analysis was performed in the development cohort to analyze risk factors. The scoring system was established accordingly and further validated in the validation cohort. Results: 1203 patients were enrolled in the study, among whom the older adult group accounted for 36% with a mean age of 71 years. The older adult group had a worse prognosis, a higher mortality rate, a higher rate of end-stage renal disease, and worsening renal function. Logistic regression showed that age &gt; 75 years, chronic heart disease, and elevated serum creatinine and D-dimer values were risk factors for poor prognosis in patients with AAV. The development and validation cohorts in patients with AAV produced area under the curve values of 0.82 (0.78–0.86) and 0.83 (0.77–0.89), respectively. Conclusion: We established a risk-scoring system based on baseline clinical characteristics to predict composite renal outcomes in patients with AAV. Our results suggest that more attention should be paid to older patients with severe renal impairment and active inflammation.
DOI: 10.6084/m9.figshare.22029554
title: AJP-Supplementary Table

description: The primer sequences for purinergic receptors
DOI: 10.6084/m9.figshare.22029554.v1
title: AJP-Supplementary Table

description: The primer sequences for purinergic receptors
DOI: 10.5519/qd.pvil25qz
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (3259 records)
DOI: 10.17909/j2ew-nh46
title: Data from paper
DOI: 10.6084/m9.figshare.24124836
title: Chat-IELTS reflective diary

description: This dataset contains learners' reflective diaries on the application of ChatGPT in practicing IELTS speaking.
DOI: 10.6084/m9.figshare.24124860
title: Generative AI policies for academic publishers

description: This Microsoft Excel file contains data downloaded from a variety of academic publishers related to their policies on how authors can use Generative AI tools when submitting work for publication. The data was collected in July 2023 and the authors make no claims as to the present accuracy of the data.
DOI: 10.6084/m9.figshare.24124866
title: Predicted coding sequences of the Pacific saury

description: Predicted coding sequences of the Pacific saury
DOI: 10.6084/m9.figshare.24124866.v1
title: Predicted coding sequences of the Pacific saury

description: Predicted coding sequences of the Pacific saury
DOI: 10.5281/zenodo.8336706
title: Dataset for Posiform Planting: Generating QUBO Instances for Benchmarking

description: Dataset for the paper titled Posiform Planting: Generating QUBO Instances for Benchmarking https://arxiv.org/abs/2308.05859 LA-UR-23-29274
DOI: 10.5281/zenodo.8336707
title: Dataset for Posiform Planting: Generating QUBO Instances for Benchmarking

description: Dataset for the paper titled Posiform Planting: Generating QUBO Instances for Benchmarking https://arxiv.org/abs/2308.05859 LA-UR-23-29274
DOI: 10.6084/m9.figshare.24124914
title: Predicted peptide sequences of the Pacific saury

description: Predicted peptide sequences of the Pacific saury
DOI: 10.6084/m9.figshare.24124914.v1
title: Predicted peptide sequences of the Pacific saury

description: Predicted peptide sequences of the Pacific saury
DOI: 10.5281/zenodo.8336920
title: Ballistic PbTe nanowire devices

description: This repository contains the raw data and processing code of the paper "Ballistic PbTe nanowire devices".
DOI: 10.5281/zenodo.8336919
title: Ballistic PbTe nanowire devices

description: This repository contains the raw data and processing code of the paper "Ballistic PbTe nanowire devices".
DOI: 10.6084/m9.figshare.22086281
title: Quantifying evolution of soot mixing state from transboundary transport of biomass burning emissions

description: Observation in Southeast Tibetan Plateau
DOI: 10.6084/m9.figshare.22086281.v2
title: Quantifying evolution of soot mixing state from transboundary transport of biomass burning emissions

description: Observation in Southeast Tibetan Plateau
DOI: 10.6084/m9.figshare.22086281.v1
title: Evolution of Size-resolved Mixing State of Soot Particles in the Ageing Process

description: Observation in Southeast Tibetan Plateau
DOI: 10.21227/vey5-hd20
title: FIR Human

description: This dataset contains video-clips of five volunteers developing daily life activities. Each video-clip is recorded with a Far InfraRed (FIR) camera and includes an associated file which contains the three-dimensional and two-dimensional coordinates of the main body joints in each frame of the clip. This way, it is possible to train human pose estimation networks using FIR imagery.It contains over 250.000 2D and 3D human poses and their corresponding FIR images. The dataset is recorded by 5 volunteers (4 males, 1 female) engaged in 27 different action classes, including falls of different kinds.The FIR video-clips are recorded at 23.98 frames per second with a resolution of 480 x 640 pixels. The annotations associated with them include accurate 3D positions of the 19 main body joints provided by a high-speed motion capture system and their projections onto the image plane.
DOI: 10.6084/m9.figshare.24124986
title: Supplementary Material for: Radiomics Correlation to CD68+ Tumor-Associated Macrophages in Clear Cell Renal Cell Carcinoma

description: Introduction: Renal cell carcinoma (RCC) is the ninth most common cancers worldwide, with clear cell RCC (ccRCC) being the most frequent histological subtype. The tumor immune microenvironment (TIME) of ccRCC is an important factor to guide treatment, but current assessments are tissue-based, which can be time-consuming and resource-intensive. In this study, we used radiomics extracted from clinically performed computed tomography (CT) as a non-invasive surrogate for CD68 tumor-associated macrophages (TAMs), a significant component of ccRCC TIME. Methods: TAM population was measured by CD68+/PanCK+ ratio and tumor-TAM clustering was measured by normalized K function was calculated from multiplex immunofluorescence (mIF). A total of 1,076 regions on mIF slides from 78 patients were included. Radiomic features were extracted from multiphase CT of the ccRCC tumor. Statistical machine learning models, including Random Forest, AdaBoost, and ElasticNet, were used to predicted TAM population and tumor-TAM clustering. Results: The best models achieved an AUROC of 0.81 (95% CI: [0.69, 0.92]) for TAM population and 0.77 (95% CI: [0.66, 0.88]) for tumor-TAM clustering, respectively. Conclusion: Our study demonstrates the potential of using CT radiomics derived imaging markers as a surrogate for assessment of TAM in ccRCC for real time treatment response monitoring and patient selection for targeted therapies and immunotherapies.
DOI: 10.6084/m9.figshare.24124986.v1
title: Supplementary Material for: Radiomics Correlation to CD68+ Tumor-Associated Macrophages in Clear Cell Renal Cell Carcinoma

description: Introduction: Renal cell carcinoma (RCC) is the ninth most common cancers worldwide, with clear cell RCC (ccRCC) being the most frequent histological subtype. The tumor immune microenvironment (TIME) of ccRCC is an important factor to guide treatment, but current assessments are tissue-based, which can be time-consuming and resource-intensive. In this study, we used radiomics extracted from clinically performed computed tomography (CT) as a non-invasive surrogate for CD68 tumor-associated macrophages (TAMs), a significant component of ccRCC TIME. Methods: TAM population was measured by CD68+/PanCK+ ratio and tumor-TAM clustering was measured by normalized K function was calculated from multiplex immunofluorescence (mIF). A total of 1,076 regions on mIF slides from 78 patients were included. Radiomic features were extracted from multiphase CT of the ccRCC tumor. Statistical machine learning models, including Random Forest, AdaBoost, and ElasticNet, were used to predicted TAM population and tumor-TAM clustering. Results: The best models achieved an AUROC of 0.81 (95% CI: [0.69, 0.92]) for TAM population and 0.77 (95% CI: [0.66, 0.88]) for tumor-TAM clustering, respectively. Conclusion: Our study demonstrates the potential of using CT radiomics derived imaging markers as a surrogate for assessment of TAM in ccRCC for real time treatment response monitoring and patient selection for targeted therapies and immunotherapies.
DOI: 10.6084/m9.figshare.24124980
title: Predset_2020.csv

description: Prediction set of AdaSTEM model, year 2020.
DOI: 10.6084/m9.figshare.24124989
title: Supplementary Material for: Radiomics Correlation to CD68+ Tumor-Associated Macrophages in Clear Cell Renal Cell Carcinoma

description: Introduction: Renal cell carcinoma (RCC) is the ninth most common cancers worldwide, with clear cell RCC (ccRCC) being the most frequent histological subtype. The tumor immune microenvironment (TIME) of ccRCC is an important factor to guide treatment, but current assessments are tissue-based, which can be time-consuming and resource-intensive. In this study, we used radiomics extracted from clinically performed computed tomography (CT) as a non-invasive surrogate for CD68 tumor-associated macrophages (TAMs), a significant component of ccRCC TIME. Methods: TAM population was measured by CD68+/PanCK+ ratio and tumor-TAM clustering was measured by normalized K function was calculated from multiplex immunofluorescence (mIF). A total of 1,076 regions on mIF slides from 78 patients were included. Radiomic features were extracted from multiphase CT of the ccRCC tumor. Statistical machine learning models, including Random Forest, AdaBoost, and ElasticNet, were used to predicted TAM population and tumor-TAM clustering. Results: The best models achieved an AUROC of 0.81 (95% CI: [0.69, 0.92]) for TAM population and 0.77 (95% CI: [0.66, 0.88]) for tumor-TAM clustering, respectively. Conclusion: Our study demonstrates the potential of using CT radiomics derived imaging markers as a surrogate for assessment of TAM in ccRCC for real time treatment response monitoring and patient selection for targeted therapies and immunotherapies.
DOI: 10.6084/m9.figshare.24124989.v1
title: Supplementary Material for: Radiomics Correlation to CD68+ Tumor-Associated Macrophages in Clear Cell Renal Cell Carcinoma

description: Introduction: Renal cell carcinoma (RCC) is the ninth most common cancers worldwide, with clear cell RCC (ccRCC) being the most frequent histological subtype. The tumor immune microenvironment (TIME) of ccRCC is an important factor to guide treatment, but current assessments are tissue-based, which can be time-consuming and resource-intensive. In this study, we used radiomics extracted from clinically performed computed tomography (CT) as a non-invasive surrogate for CD68 tumor-associated macrophages (TAMs), a significant component of ccRCC TIME. Methods: TAM population was measured by CD68+/PanCK+ ratio and tumor-TAM clustering was measured by normalized K function was calculated from multiplex immunofluorescence (mIF). A total of 1,076 regions on mIF slides from 78 patients were included. Radiomic features were extracted from multiphase CT of the ccRCC tumor. Statistical machine learning models, including Random Forest, AdaBoost, and ElasticNet, were used to predicted TAM population and tumor-TAM clustering. Results: The best models achieved an AUROC of 0.81 (95% CI: [0.69, 0.92]) for TAM population and 0.77 (95% CI: [0.66, 0.88]) for tumor-TAM clustering, respectively. Conclusion: Our study demonstrates the potential of using CT radiomics derived imaging markers as a surrogate for assessment of TAM in ccRCC for real time treatment response monitoring and patient selection for targeted therapies and immunotherapies.
DOI: 10.6084/m9.figshare.24124998
title: Supplementary Material for: Mismatch repair deficiency in biliary tract cancer: prognostic implications and correlation with histology.

description: Introduction: Mismatch repair (MMR) deficiency represents a biomarker and therapeutic target in various neoplasms, but its role in biliary tract cancers (BTCs) remains misunderstood. Methods: MMR status was retrospectively assessed using immunohistochemistry in 163-BTCs patients. We identified MMR proficiency (pMMR)/deficiency (dMMR) according to the loss of MMR proteins (MLH-1, PMS-2, MSH-2, MSH-6). The primary objective of the study was to assess the incidence of dMMR in BTCs; the secondary purpose was to explore its association with prognosis and clinical features. Results: dMMR was recorded in 9 patients and it was strongly associated with mucinous histology (p&lt;0.01). Regarding the prognostic effect, in 122-radically resected patients, disease-free-survival (DFS) resulted significantly shorter in dMMR-patients compared to pMMR-patients (10.7 vs 31.3 months, p = 0.025) and so did nodal status (48.2 vs 15.3 months in N0 vs N+) (p &lt; 0.01). Moreover, dMMR confirmed its prognostic role in terms of DFS at multivariate analysis (p = 0.03), together with nodal status (p = 0.01) and resection margin (p = 0.03). In 103 M+ patients (encompassing 41 metastatic de novo and 62 recurred after surgery patients) there weren’t differences between dMMR and pMMR regarding survival analyses. Discussion/Conclusions: dMMR status is strongly correlated with mucinous histology and represents an independent prognostic factor in terms of disease-relapse in patients with resected BTC.
DOI: 10.6084/m9.figshare.24124998.v1
title: Supplementary Material for: Mismatch repair deficiency in biliary tract cancer: prognostic implications and correlation with histology.

description: Introduction: Mismatch repair (MMR) deficiency represents a biomarker and therapeutic target in various neoplasms, but its role in biliary tract cancers (BTCs) remains misunderstood. Methods: MMR status was retrospectively assessed using immunohistochemistry in 163-BTCs patients. We identified MMR proficiency (pMMR)/deficiency (dMMR) according to the loss of MMR proteins (MLH-1, PMS-2, MSH-2, MSH-6). The primary objective of the study was to assess the incidence of dMMR in BTCs; the secondary purpose was to explore its association with prognosis and clinical features. Results: dMMR was recorded in 9 patients and it was strongly associated with mucinous histology (p&lt;0.01). Regarding the prognostic effect, in 122-radically resected patients, disease-free-survival (DFS) resulted significantly shorter in dMMR-patients compared to pMMR-patients (10.7 vs 31.3 months, p = 0.025) and so did nodal status (48.2 vs 15.3 months in N0 vs N+) (p &lt; 0.01). Moreover, dMMR confirmed its prognostic role in terms of DFS at multivariate analysis (p = 0.03), together with nodal status (p = 0.01) and resection margin (p = 0.03). In 103 M+ patients (encompassing 41 metastatic de novo and 62 recurred after surgery patients) there weren’t differences between dMMR and pMMR regarding survival analyses. Discussion/Conclusions: dMMR status is strongly correlated with mucinous histology and represents an independent prognostic factor in terms of disease-relapse in patients with resected BTC.
DOI: 10.6084/m9.figshare.24124974
title: Data and supporting information for "Trace and rare earth element fingerprints of aerobic oxidation of methane in seep-dwelling bivalves"

description: This directory contains all the raw data and supporting information for "<b><i>Trace and rare earth element fingerprints of aerobic oxidation of methane in seep-dwelling bivalves</i></b>", which are stored in xlsx format.
DOI: 10.6084/m9.figshare.24124974.v1
title: Data and supporting information for "Trace and rare earth element fingerprints of aerobic oxidation of methane in seep-dwelling bivalves"

description: This directory contains all the raw data and supporting information for "<b><i>Trace and rare earth element fingerprints of aerobic oxidation of methane in seep-dwelling bivalves</i></b>", which are stored in xlsx format.
DOI: 10.5281/zenodo.7956732
title: Metadane dla Energy-Efficient OFDM Radio Resource Allocation Optimization With Computational Awareness: A Survey

description: Metadata for Energy-Efficient OFDM Radio Resource Allocation Optimization With Computational Awareness: A Survey
description: Sfinansowane przez Narodowe Centrum Nauki w ramach projektu Świeże i zielone komórkowe sieci obliczeniowe IoT - FitNets o numerze 2021/41/N/ST7/03941.
DOI: 10.5281/zenodo.7956731
title: Metadane dla Energy-Efficient OFDM Radio Resource Allocation Optimization With Computational Awareness: A Survey

description: Metadata for Energy-Efficient OFDM Radio Resource Allocation Optimization With Computational Awareness: A Survey
description: Sfinansowane przez Narodowe Centrum Nauki w ramach projektu Świeże i zielone komórkowe sieci obliczeniowe IoT - FitNets o numerze 2021/41/N/ST7/03941.
DOI: 10.5281/zenodo.7428434
title: Datasets to Evaluate Accuracy, Miscalibration and Popularity Lift in Recommendations

description: This repository contains three datasets for evaluating accuracy, miscalibration and popularity lift in recommender systems. All datasets contain genre/category information in addition to different user group splits: Last.fm (lfm.zip), based on the LFM-1b dataset of JKU Linz (http://www.cp.jku.at/datasets/LFM-1b/) MovieLens (ml.zip), based on MovieLens-1M dataset (https://grouplens.org/datasets/movielens/1m/) MyAnimeList (anime.zip), based on the MyAnimeList dataset of Kaggle (https://www.kaggle.com/CooperUnion/anime-recommendations-database) 'user_events_cats.txt' contains the users' rating/interaction data along with a list of genres/categories assigend to the rated items. The list of categories is given in 'categories.txt'. Additionally, assignments to three user groups that differ in their inclination to popular/mainstream items are provided: LowPop in 'low_main_users.txt', MedPop in 'med_main_users.txt', and HighPop in 'high_main_users.txt'. The format of the three user files are "user,mainstreaminess" The format of the user-events files are "user,item,preference,cats", where different categories are separated by '|' The format of the categories files are "category-name,index", where index refers to the category-id in the user-events files Example Python-code for analyzing the datasets as well as empirical results on calibration, popularity lift and accuracy can be found on GitHub: https://github.com/domkowald/FairRecSys