cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/8gpvhrrghk
title: TGN dataset

description: Step-by-Step Approach
Verify Dataset and Annotations:

Double-check that your annotations.csv file contains valid entries with correct paths (image_path) and corresponding labels (label).
Load and Inspect Data:

Make sure that df_annotations is correctly loaded and contains data. Print or inspect df_annotations.head() to see the first few rows and verify its structure.
Debug and Visualize:

Implement error handling and debugging statements to pinpoint where the issue might arise. Ensure that image paths are correctly formed and that images can be loaded using matplotlib.
Display the Visualization:

Use matplotlib to display the images along with their labels, ensuring the plot dimensions are correctly set.

To implement Mask R-CNN with annotated data, we'll need to follow several steps including preparing the data, configuring the Mask R-CNN model, training it on the annotated dataset, and then using it for inference. Here’s a structured approach to accomplish this:

Step-by-Step Guide to Implement Mask R-CNN with Annotated Data
1. Prepare the Dataset
First, ensure your annotated data is structured correctly. This typically involves:

Annotations: Each image should have corresponding annotations specifying the regions of interest (ROIs) and their labels.
Image Loading: Load images and annotations into a format suitable for training Mask R-CNN.
2. Install Required Libraries
Ensure you have the necessary libraries installed:
3. Customize Mask R-CNN for Your Dataset
You might need to customize the Mask R-CNN configuration (mrcnn/config.py) for your specific dataset. This involves setting parameters such as:

Number of classes (NUM_CLASSES)
Image resizing parameters
Backbone network (default is ResNet101)
4. Prepare Data Loaders
Create data loaders to feed data into Mask R-CNN:

Dataset Class: Implement a custom dataset class (mrcnn/utils.py) that loads images and annotations.
Data Generator: Create a data generator that preprocesses images and annotations for training.
5. Configure and Train the Model
Configure and train Mask R-CNN using your annotated dataset:

Model Configuration: Set up the Mask R-CNN model (mrcnn/model.py) with your customized configuration.
Training: Train the model using the prepared data loaders and appropriate training parameters (mrcnn/train.py).
6. Evaluate and Fine-Tune
After initial training, evaluate the model's performance:

Validation: Evaluate on a validation set to assess metrics such as mAP (mean Average Precision).
Fine-Tuning: Fine-tune hyperparameters or adjust the model architecture based on validation results.
7. Inference
Use the trained Mask R-CNN model for inference on new data:

Detection and Segmentation: Apply the model to detect and segment objects in new images.
Visualization: Visualize the results of the model's predictions.
DOI: 10.17632/hrw3xjfdgy
title: Ultrasonic measurements for evaluating elastic properties in 3D-printed 316L stainless steel parts

description: These are the raw ultrasonic waveform files and nanoindentation measurements for three additively manufactured 316L stainless steel components with different fabrication parameters. Each part's length is divided into four segmented regions where their fabrication energy densities change. Part V+ begins at 33 J/mm^3 and increases in energy density by 3 J/mm^3 with each segment. Part C remains at a constant 33 J/mm^3 throughout the part. Part V- begins at 33 J/mm^3 and decreases in energy density by 3 J/mm^3 with each segment. 

We have found that the ultrasonic waves in regions with higher energy densities will traverse more quickly, as shown by the shorter time of flight, and vice versa. Similarly, nanoindentation measurements of reduced modulus and hardness follow this trend.

We have measured the direction in which the parts were fabricated (build direction) and the segmented regions that are perpendicular to the build direction (transverse directions). The build direction longitudinal wave velocity measurements are considerably lower than their transverse direction counterparts, indicating anisotropy between the two directions.

In Part C, we have removed material from one of its surfaces and recorded its ultrasonic and nanoindentation measurements with each material removal iteration. Changes in the material properties are more prominent by nanoindentation, suggesting material heterogeneity between the part's surface and interior.

The README.txt file has information on how to navigate the files and process the data.
DOI: 10.6084/m9.figshare.27897768.v1
title: CaseF1-F12.py

description: 河流流量估算对于有效的水资源管理至关重要。然而，水文站的高运营和维护支出导致流量监测数据减少。使用迁移学习通过少量数据点估算河流流量，为补充该领域的现有观测数据具有巨大潜力。
DOI: 10.6084/m9.figshare.5660398.v3
title: Allometric Trophic Niche Network model outputs and processing code from: On the prevalence and dynamics of inverted trophic pyramids and otherwise top-heavy communities

description: This zip file contains raw and processed data files from Allometric Trophic Niche Network modeling. Details of the models are in the paper's methods or supplements.
DOI: 10.6084/m9.figshare.7094627.v1
title: Healthcare of the floating population in China

description: Dataset of healthcare of the floating population in China, 2014
DOI: 10.6084/m9.figshare.9956477.v1
title: bacterial GWAS benchmark simulations: High LD dataset

description: Supplementary data for the paper "Benchmarking bacterial genome-wide association study (GWAS) methods using simulated genomes and phenotypes": https://www.biorxiv.org/content/10.1101/795492v1
DOI: 10.17632/gvh2vtg86r
title: Deciphering the Signaling Network Landscape of Breast Cancer Improves Drug Sensitivity Prediction
DOI: 10.17632/2nmr2cz9yy
title: Datasets for "Multiscalar 3D-temporal structural characterisation of Smøla Island, Mid-Norwegian passive margin" Authored by Hodge et al.

description: A repository of support datasets for "Multiscalar 3D-temporal structural characterisation of Smøla Island, Mid-Norwegian passive margin: an analogue for unravelling the tectonic history of offshore basement highs", authored by: Hodge et al. originally submitted 2023, resubmitted 2024 - Solid Earth
DOI: 10.17632/djr93wwgj3
title: Bogaziçi University Smartphone Accelerometer Sensor Dataset

description: Mobile devices especially smartphones have gained high popularity and become a part of daily life in recent years. Smartphones have built-in motion sensors such as accelerometer, gyroscope and orientation sensors. Recent researches on smartphones show that behavioral biometrics can be obtained from the smartphone motion sensors. In this context, we develop an Android application that collects accelerometer sensor data while user playing a game. This application records all accelerometer data and touch event information while users touch the screen.  We perform two experiments and collect two different data using this application. In the first experiment, we collect data from 107 child users whose age vary from 4 to 11, and 100 adult users whose age are between 16 and 55. This dataset includes more than 11.000 taps data for child and adult users, in total. In the second experiment, data is collected from 60 female and 60 male users aged 17-57 for different activities like sitting and walking. There are more than 6.000 taps data for sitting and walking scenarios separately in the second dataset. We use popular Android smartphones in the experiments and they have all 100 Hz sampling rate. These data can be used for behavioral biometric analyses such as user age-group and gender detection, user identification and authentication or tap event detection.
DOI: 10.17632/mypxw5zbct
title: metadados Vanessa

description: NaNbO3@WO3
DOI: 10.17632/wskxw3bnk2
title: Bacterial communities in an intermittent piped water supply in Arraiján, Panama

description: Intermittent water supply (IWS) is a common practice in low- and middle-income countries around the world, and has been shown to degrade water quality. In this study, we characterized water quality and bacterial communities in a section of the drinking water distribution network in Arraiján, Panama with IWS, in which service alternated between several days with and without supply. We sampled at two locations in this zone, one near the entrance to the IWS zone (with continuous supply) and one at a downstream location (with IWS). 16S rDNA amplicon sequencing was used to characterize bacterial communities in the bulk water.
DOI: 10.6084/m9.figshare.26936401.v1
title: Test-retest reliability and comparison of portable Kinvent force platform with contact mat and video-based software for measuring countermovement jump height

description: The countermovement jump (CMJ) is one of the most common tests used by practitioners to assess lower-body explosive capacities. This study aimed to (a) assess the test-retest reliability of the portable K-Deltas force platforms to measure the CMJ height and (b) to compare the force platform-derived jump height to the contact mat (Chronojump, Spain) and a video-based software (My Jump app).
DOI: 10.17632/bjpjr25gdj
title: Data for: The impact of social distancing on early SARS-CoV-2 transmission in the United States

description: Citation: 
Sanchez, JN, GA Reyes, BM Lopez, CK Johnson. 2022. The impact of social distancing on early SARS-CoV-2 transmission in the United States. Zoonoses and Public Health. https://doi. org/10.1111/zph.12909

Abstract:
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a viral pathogen that quickly became a global pandemic in the winter of 2020 – 2021. In response, governments issued social distancing orders to minimize transmission by reducing community contacts. We tested the efficacy of this social distancing at the state level during the first two months of the pandemic in the United States. We utilized data on daily SARS-CoV-2 case numbers and human community mobility (anonymized, aggregated cellphone location data stratified into six categories used as an index of social distancing), the date of government-issued social distancing orders, demographics, urbanization, and public transportation. We implemented cross-correlation to identify lag times between declines in mobility and SARS-CoV-2 cases. Incorporating state-specific lag times, we tested for associations between case counts and mobility metrics using Bayesian multilevel models. Decreased mobility around grocery stores/pharmacies, retail/recreation locations, transit stations, and workplaces were correlated with decreases in SARS-CoV-2 cases with significant lag times of ≥21 days. Social distancing orders were associated with fewer cumulative SARS-CoV-2 cases when they were put in place earlier. Community mobility had already started declining prior to most social distancing orders, especially the more restrictive orders implemented later in the pandemic. Social distancing is an important tool that has been implemented throughout the pandemic to decrease SARS-CoV-2 transmission, although with significant social and economic impacts. Our results suggest that declines in cases were observed several weeks subsequent to implementation of social distancing measures, and that implementing social distancing earlier could potentially minimize the duration of time these policies need to be in effect. Our findings can inform ongoing management of this pandemic and other emerging infectious disease outbreaks by identifying areas where reductions in mobility are associated with reduced disease transmission, and the expected time frame between behavioral changes and measurable population outcomes.
DOI: 10.17632/v54ndz5vg4
title: Data for: A Longitudinal Study of Maternal Feeding and Children’s Picky Eating

description: THis is the data set as used in the analyses presented in this paper minus identifying information
DOI: 10.6084/m9.figshare.11391558.v1
title: TEMPERATURE EFFECTS ON RAMAN SPECTRA OF PEDOT AND MEH-PPV CONDUCTING POLYMERS

description: The present work shows the temperature dependence of the Raman spectra of two conjugated polymers: poly[2-methoxy-5-(2-ethylhexyloxy)-1,4-phenylenevinylene] (MEH-PPV) and poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS). In situ resonance Raman spectra of PEDOT from 77 to 423 K at 633 nm and of MEH-PPV from 77 to 473 K at 785 nm suggest that, for both polymers, as the temperature increases there is a decrease in the chain conjugation length. Raman bands of PEDOT assigned to the νsim Cα-Cβ are the most sensitive to temperature while bands due to oxyethylene rings do not show significant shifts in this temperature range. Raman and FT-IR spectra of MEH-PPV recorded above the thermal decomposition temperature provide some insights about the thermal degradation.
DOI: 10.6084/m9.figshare.23257013.v1
title: Supplementary raw sequence data

description: The effects of warming, precipitation reduction and their combination on soil bacterial and fungal communities in a semi-arid grassland on the Loess Plateau.
DOI: 10.6084/m9.figshare.26504206.v1
title: pig manure and soil viral datasets

description: pig manure and soil viral datasets
DOI: 10.17632/7dtj7f7849
title: The formation of ferromanganese crusts from the Western Mariana Ridge and implications for deep-water environment since the late Pliocene

description: The geochemical compositions of ferromanganese crusts from the Western Mariana Ridge.
DOI: 10.6084/m9.figshare.23613627.v1
title: Metatasks for Auto-Sklearn 1 - ROC AUC and Balanced Accuracy

description: Prediction Data of Base Models from Auto-Sklearn 1 on 71 classification datasets from the AutoML Benchmark for Balanced Accuracy and ROC AUC. <br> The files of this figshare item include data that was collected for the paper: <br> <strong>Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML,</strong> <em>Lennart Purucker, Lennart Schneider, Marie Anastacio, Joeran Beel, Bernd Bischl, Holger Hoos, Second International Conference on Automated Machine Learning, 2023.</em> <br> The data was stored and used with the <em>assembled </em>framework: https://github.com/ISG-Siegen/assembled. <br> In detail, the data contains the predictions of base models on validation and test as produced by running Auto-Sklearn 1 for 4 hours. Such prediction data is included for each model produced by Auto-Sklearn 1 on each fold of 10-fold cross-validation on the 71 classification datasets from the AutoML Benchmark. The data exists for two metrics (ROC AUC and Balanced Accuracy). More details can be found in the paper. <br> The data was collected by code created for the paper and is available in its <em>reproducibility repository</em>: https://doi.org/10.6084/m9.figshare.23613624. <br> Its usage is intended for but not limited to using <em>assembled </em>to evaluate post hoc ensembling methods for AutoML. <br> Details The link above points to a hosted server that facilitates the download. We opted for a hosted server, as we found no other suitable solution to share these large files (due to file size or storage limits) for a reasonable price. If you want to obtain the data in another way or know of a more suitable alternative, please contact Lennart Purucker. <br> The link resolves to a directory containing the following: <br> example_metatasks: contains an example metatask for test purposes before committing to downloading all files. metatasks_roc_auc.zip: The Metatasks obtained by running Auto-Sklearn 1 for ROC AUC. metatasks_bacc.zip: The Metatasks obtained by running Auto-Sklearn 1 for Balanced Accuracy. <br> The size after unzipping the entire file is: metatasks_roc_auc.zip: ~450GB metatasks_bacc.zip: ~330GB We suggest extracting only files that are of interest from the .zip archive, as these can be much smaller in size and might suffice for experiments. <br> The metatask .zip files contain 2 subdirectories for Metatasks produced based on TopN or SiloTopN pruning (see paper for details). In each of these subdirectories, 2 files per metatask exist. One .json file with metadata information and a .hdf or .csv file containing the prediction data. The details on how this should be read and used as a Metatask can be found in the <em>assembled </em>framework and the reproducibility repository. To obtain the data without Metataks, we advise looking at the file content and metadata individually or parsing them by using Metatasks first.
DOI: 10.6084/m9.figshare.26004538.v1
title: Data from "Pollination across the diel cycle: a global meta-analysis"

description: Code and data from the article:<b>Pollination across the diel cycle: a global meta-analysis</b>.Liam Kendall<sup>1</sup> & Charlie C. Nicholson<sup>2,3</sup><b>Affiliations</b><i>1</i> Centre for Environmental and Climate Science, Lund University, Sölvegatan 37, S-223 62 Lund, Sweden<i>2</i> Department of Biology, Lund University, Sölvegatan 37, S-223 62 Lund, Sweden<i>3</i> Department of Biology, Marine Biology, and Environmental Science, Roger Williams University, Bristol, USAContentsThis repository contains the required data and code to reproduce a global meta-analysis of diel pollination differences in flowering plants.Data (data folder) consist of diel pollination comparisons and metadata extracted directly from literature (DvN_LiteratureDataClean_2024-09-13.csv), study-site level environmental data (DvN_Site_DailyTemperatureRange.csv, DvN_Site_Daylength.csv and DvN_Sites_elevation.csv) and plant functional traits (SppTraitData_clean.csv).Scripts (script folder) consist of data curation and cleaning steps (001 - 004), model fitting (005) and model reporting and visualisation (006). After each major step, .rData files are saved within the Output folder for use in later scripts.
DOI: 10.6084/m9.figshare.28136420.v2
title: Validation of the Thai version of the World Health Organization’s Quality of Life Scale (WHOQOL-BREF-THAI) among Thai Nursing Students in Northeast Thailand: A multi-canter study

description: Background: The Thai version of World Health Organization Quality of Life Scale (WHOQOL-BREF-THAI) has been verified among a variety of populations. However, existing research lacks comprehensive psychometric properties, including factor structure, specifically among nursing students. This study examined the WHOQOL-BREF's unique psychometric features for the Thai nursing students in Northeast Thailand.Methods: A cross-sectional study recruited the Thai nursing students using multi-stage simple random sampling from 15 universities located in the Northeast of Thailand. The data was collected via self-assessment questionnaires, and the 3,570 participants were then randomly split into two subsamples (group 1, n=2,000, and group 2, n=1,570). After reducing some items using statistical methods, an exploratory factor analysis (EFA) was carried out on group 1 to explore the factor structure of the WHOQOL-BREF-THAI. Finally, group 2 was used in a confirmatory factor analysis (CFA) to validate the EFA's modified structure along with an assessment of the construct validity of the WHOQOL-BREF-THAI.Results: A total of 3,570 Thai nursing students were enrolled. Principal component analysis in a random subsample supported a four-factors model with 24 items was originally suggested for the factorial construct validity, and contained 24 items, spread across four components as follows: physical (7 items), psychological (6 items), social relationship (3 items) and environmental (8 items). The purpose domain structure showed good fit in CFA in the other subsample. Internal reliability was satisfactory (Cronbach's alpha was greater than 0.70) for both the total scale and subscales. In terms of convergent validity, average variance extracted (AVE) revealed that all WHOQOL-BREF-THAI subscales achieved convergence effect with AVE ranging from 0.50 to 0.69.Conclusion: These results reveal that the WHOQOL-BREF-THAI is a valid and reliable tool for health care providers to utilize to measure QOL among Thai nursing students. QOL is also likely to have an impact on their future clinical success. Therefore, the WHOQOL-BREF can be an essential inventory for researching the factors influencing nursing students'QOL, with implications for nursing education.<br>
DOI: 10.17632/t94tfnxzs7
title: eyes disease

description: eyes disease classification
DOI: 10.6084/m9.figshare.21767345.v1
title: Raw and preprocessed data: Talluri, Kang et al. 2022

description: Example raw data, preprocessed data to reproduce analyses in Talluri, Kang et al. 2022 bioRxiv. doi: 10.1101/2022.09.08.507006. (now in press at Nature Neuroscience).The code to reproduce the analysis is in https://github.com/NienborgLab/TalluriKang_et_al_2022.Raw and preprocessed data files are in .mat format.If you use the data or the code, please cite: Talluri, B. C., Kang I., Lazere, A., Quinn, K. R., Kaliss, N., Yates, J.L., Butts, D.A., Nienborg, H. (2022). Activity in primate visual cortex is minimally driven by spontaneous movements. bioRxiv. 2022.09.08.507006. doi: https://doi.org/10.1101/2022.09.08.507006
DOI: 10.17632/6pt94yk68b
title: Docking of spike protein of different coronaviruses with dengue virus serotype 2 envelope antibodies

description: Each folder contains HDOCK and CLUSPRO docking files.
DOI: 10.6084/m9.figshare.14548947.v1
title: Global predictions of primary soil salinization under changing climate in the 21st century

description: This dataset quantifies the annual average of naturally-occurring soil salinity to the depth of one meter from the soil surface in drylands of the world (aridity index ≤ 0.65) for the period between 1904 and 2100. This is realised by developing 4-D predictive models of the electrical conductivity of water-saturated soil paste extract (ECe). These machine learning-based predictive models predict ECe as target variable at different times and locations, enabling us to quantify different aspects of variability in primary soil salinization by the end of 21th century.
DOI: 10.6084/m9.figshare.29038880.v2
title: Photodissociation Dynamics of XH+ (X = Na, K, Rb, Cs): Rovibrationally Resolved Shape Resonances Driven by Potential Barriers in the Ultraviolet Spectrum

description: The adiabatic PECs of XH+ (X = Na, K, Rb, Cs) and the TDMs for electronic transitions from the ground state 1<sup>2</sup>Σ<sup>+</sup> to the <sup>2</sup>Σ<sup>+</sup> and <sup>2</sup>Π excited states.The total photodissociation cross sections of NaH+, KH+, RbH+ and CsH+ at thermodynamic temperatures of 0 K, 500 K, 1000 K, 5000 K and 10000 K, respectively.<br>
DOI: 10.6084/m9.figshare.5035175.v2
title: Heagy et al, 2017 - 1D FDEM and TDEM Inversions

description: Here, we perform a 1D inversion using both the frequency and time domaincodes. The forward simulations are conducted on a cylindrically symmetricmesh. The source is a point magnetic dipole source.<br>This example is used in the paper<br>Heagy, L.J., R. Cockett, S. Kang, G.K. Rosenkjaer, D.W. Oldenburg,2017 (in review), A framework for simulation and inversion in electromagnetics.Computers & Geosciences